diff --git a/CHANGELOG.md b/CHANGELOG.md
index 255ae85b..21bd0fa5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -27,6 +27,26 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
allowlisted `NameError`s (`r_comparison:block2`, `troubleshooting:block8`)
are now self-contained and removed from `_CONTEXT_DEPENDENT_SNIPPETS`, so
`tests/test_doc_snippets.py` actually executes them.
+- **Tutorial notebooks migrated off the deprecated fit-time `aggregate=`**
+ (the sweep's notebook half, completing the TODO row): 16 analytical
+ fit/configuration sites across `02_staggered_did`, `16_survey_did` (including its
+ 200-draw Monte-Carlo loop), `26_composition_drift_calibration` (native
+ seam + the two `balance`-adapter sites, where the kwarg was not
+ load-bearing), `16_wooldridge_etwfe`, `17_brand_awareness_survey` and
+ `24_staggered_vs_collapsed_power` now fit plain and aggregate post-fit,
+ with legacy `event_study_effects`/`group_effects` dict reads rebound to
+ the containers (keyed lookups preserve each notebook's missing-event-time
+ fallbacks; cells that printed the fit-time `summary()` aggregation blocks
+ print the container summaries instead, so no rendered teaching content is
+ lost). The BOOTSTRAPPED fits (`02` cell 20, both `09_real_world_examples`
+ case studies) deliberately KEEP fit-time `aggregate=` with comments naming
+ it the documented until-4.0 exception, and `08_triple_diff`'s staggered
+ DDD keeps it as the canonical route (M-140/M-141).
+ `tests/test_t26_composition_drift_calibration_drift.py` mirrors the t26
+ migration at all three of its sites. All edited notebooks re-executed
+ under `DIFF_DIFF_BACKEND=python` (the CI/RTD backend); notebooks stored
+ in compact JSON were normalized to nbformat's canonical layout by the
+ write-back.
## [3.9.0] - 2026-08-10
diff --git a/TODO.md b/TODO.md
index 67fbbbf3..74447637 100644
--- a/TODO.md
+++ b/TODO.md
@@ -21,7 +21,7 @@ Related tracking surfaces:
| Issue | Location | Origin | Effort | Priority |
|-------|----------|--------|--------|----------|
-| Post-fit `aggregate()` for the staggered DDD container: `StaggeredTripleDiffResults` carries no `AggregationMixin`, which is why the phase-3(b) merge had to carry fit-time `aggregate=`/`balance_e=` onto the surviving `TripleDifference` (rows M-140/M-141) as the ONE documented exception to the section-6 aggregate-postfit program. Porting the container onto the M-122 aggregation contract retires both rows; note the bootstrapped-fit recompute levels will need draw retention or a fail-closed relay, the same problem tracked for CS/EfficientDiD/ImputationDiD | `diff_diff/staggered_triple_diff_results.py`, `diff_diff/aggregation.py` | 3(b) | Heavy | Medium |
+| Post-fit `aggregate()` for the staggered DDD container: `StaggeredTripleDiffResults` carries no `AggregationMixin`, which is why the phase-3(b) merge had to carry fit-time `aggregate=`/`balance_e=` onto the surviving `TripleDifference` (rows M-140/M-141) as the ONE documented exception to the section-6 aggregate-postfit program. Porting the container onto the M-122 aggregation contract retires both rows; note the bootstrapped-fit recompute levels will need draw retention or a fail-closed relay, the same problem tracked for CS/EfficientDiD/ImputationDiD. Until it lands, the DDD docs deliberately keep teaching the fit-time kwarg (the canonical route there) | `diff_diff/staggered_triple_diff_results.py`, `diff_diff/aggregation.py`, `docs/api/triple_diff.rst`, `docs/tutorials/08_triple_diff.ipynb` | 3(b) | Heavy | Medium |
| Staggered-DDD power support: `simulate_power`/`simulate_mde`/`simulate_sample_size` now REJECT a staggered-configured `TripleDifference` (both registered DDD generators emit 2x2x2 data and fit with `(group, partition, post)`, so a staggered config would be simulated under the wrong design). Support needs a staggered DDD DGP profile plus fit-kwargs builder, and a decision on whether the mode is selected by profile or by the estimator's own config | `diff_diff/power.py` | 3(b) | Mid | Low |
| Bootstrap-`seed` provenance on multiplier-bootstrap results containers: neither `StaggeredTripleDiffResults` nor `CallawaySantAnnaResults` carries the `seed` that generated its bootstrap SEs / p-values / sup-t bands, so a serialized result cannot report the random configuration behind its inference. NOT a 3(b) regression - `seed` reaches the engine and `get_params()` correctly (same seed reproduces the SE bit-exactly, a different seed moves it), the gap is results-object observability only, it predates the merge, and both containers inherit it from the shared `CallawaySantAnnaBootstrapMixin`. Add `seed` (and consider `n_bootstrap`/`bootstrap_weights`/`cband`) to BOTH containers plus `to_dict()`, with seeded and unseeded pins; sequence it with the M-014 container unification rather than schema-changing one container mid-merge. Precedent for exposing it: `ContinuousDiDResults`, `EfficientDiDResults`, `SyntheticDiDResults` already do | `diff_diff/staggered_triple_diff_results.py`, `diff_diff/staggered_results.py` | 3(b) | Quick | Low |
| Library-wide `anticipation` domain validation: `TripleDifference` now rejects non-integral / negative / `bool` windows at construction (phase 3(b)) because the value feeds BOTH the base-period rule and the not-yet-treated threshold, so `anticipation=-1` silently makes the universal base period `g` (already treated) and admits cohorts treated at the evaluation period as clean controls. Only `spillover.py` and `wooldridge.py` validate it today (and neither rejects `bool`, which coerces to a silent one-period window); `CallawaySantAnna`, `SunAbraham`, `ImputationDiD`, `TwoStageDiD`, `StackedDiD`, `ContinuousDiD`, `EfficientDiD` and the deprecated `StaggeredTripleDifference` do not. The shared validator now EXISTS - `utils.validate_anticipation`, adopted by `TripleDifference.__init__` and by the staggered engine (so `StaggeredTripleDifference` fails closed at fit too); aligning the remaining seven estimators is a matter of calling it from each constructor | `diff_diff/staggered.py`, `diff_diff/sun_abraham.py`, `diff_diff/imputation.py`, `diff_diff/two_stage.py`, `diff_diff/stacked_did.py`, `diff_diff/continuous_did.py`, `diff_diff/efficient_did.py`, `diff_diff/spillover.py`, `diff_diff/wooldridge.py` | 3(b) | Mid | Medium |
@@ -35,7 +35,7 @@ Related tracking surfaces:
| EfficientDiD `aggregate()` recompute levels (event_study/group) on bootstrapped fits fail closed ('simple' relays since the M-027 per-level convergence); wiring `BootstrapReplaySpec` (or retaining the n_bootstrap x n_gt draw matrix materialized at fit) would enable exact post-fit replay of percentile inference | `diff_diff/efficient_did_results.py`, `diff_diff/aggregation.py` | 2(b) PR-3a | Mid | Low |
| ImputationDiD/TwoStageDiD `aggregate()` recompute levels on bootstrapped fits fail closed ('simple' relays since the M-027 per-level convergence; M-021/M-022); ImputationDiD's per-target psi machinery makes seeded replay tractable (the panel-backed kit retains everything the psi precompute reads), TwoStageDiD's per-level GMM scores are function-locals and would need retention | `diff_diff/imputation_results.py`, `diff_diff/two_stage_results.py`, `diff_diff/aggregation.py` | 2(b) PR-3b | Mid | Low |
| ContinuousDiD `aggregate('event_study')` on bootstrapped fits fails closed (M-025); a seeded post-fit bootstrap-ES replay is tractable - the multiplier draws are seeded (`np.random.default_rng(self.seed)`) - but needs the FULL per-cell `_bootstrap_info` (bread/ee_treated/Psi_eval/dPsi_*/beta_pred) the pruned kit deliberately drops, so shipping it means a kit-payload change with its own memory contract | `diff_diff/continuous_did_aggregation.py`, `diff_diff/continuous_did_results.py` | 2(b) PR-3c | Mid | Low |
-| diagnostic_report's ES-gated checks read the raw `event_study_effects` field, which post-fit `results.aggregate()` never populates - their remediation strings steer users to the deprecated fit-time kwarg (qualified "deprecated but functional until 4.0" since 2(b) PR-3b); teach the checks to consume a post-fit container (or recompute via the kit) before 4.0 removes the kwarg | `diff_diff/diagnostic_report.py` | 2(b) PR-3b | Mid | Medium |
+| diagnostic_report's ES-gated checks read the raw `event_study_effects` field, which post-fit `results.aggregate()` never populates - their remediation strings steer users to the deprecated fit-time kwarg (qualified "deprecated but functional until 4.0" since 2(b) PR-3b); teach the checks to consume a post-fit container (or recompute via the kit) before 4.0 removes the kwarg. The report API pages deliberately keep their fit-time examples until this lands (post-fit-aggregated results would produce empty ES read-outs) | `diff_diff/diagnostic_report.py`, `docs/api/business_report.rst`, `docs/api/diagnostic_report.rst` | 2(b) PR-3b | Mid | Medium |
| EfficientDiD, ImputationDiD, ContinuousDiD and HeterogeneousAdoptionDiD are the outstanding M-092 event-study df-provenance holes: the container's per-row df is all-NaN even on survey fits where a finite `_survey_df` governed the p-values (the container-level scalar `df_survey` IS exposed - the hole is the PER-ROW column only; no event_study_df/df_inference field; pre-existing, NOT a regression of the M-023 PR - today's builder output is identical). The kits now retain the scalar (ImputationDiD's since 2(b) PR-3b, ContinuousDiD's since 2(b) PR-3c - same shape: scalar `df_survey` exposed, per-row column all-NaN, identical to each fit-time surface); threading it into the per-row channel is a contained follow-up | `diff_diff/efficient_did_results.py`, `diff_diff/imputation_results.py`, `diff_diff/continuous_did_results.py`, `diff_diff/results_base.py` | 2(b) PR-3a | Quick | Low |
| practitioner `step_name="heterogeneity"` producer-side collisions: three OTHER estimators' advice steps reuse the key with non-heterogeneity labels (`:975` ContinuousDiD dose-response, `:1022` Triple placebo-group, `:1413` LPDiD WAS arrays), so DiagnosticReport's heterogeneity completion silently drops that unrelated advice from `next_steps` via `_filter_steps` - the same latent collision fixed for StackedDiD in M-024 (renamed to `sub_experiment_balance`). Renaming these changes those estimators' report output; audit + rename with per-estimator pins. | `diff_diff/practitioner.py` | 2(b) PR-2 review R9 | Quick | Low |
| PreTrendsPower `violation='linear'` on CS `base_period='varying'` input targets the wrong alternative: `δ_pre = M · \|t\|` assumes level coefficients against a common reference, but varying-base pre-treatment effects are consecutive-period comparisons (constant increments under a linear trend). Both CS-sourced routes now WARN (REGISTRY PreTrendsPower Note), and universal-base GAPPED grids fail closed via the `reference_event_times` common-reference guard; what remains is the varying-base resolution - either transforming the violation vector through each coefficient's actual base mapping (needs per-horizon base provenance) or requiring `base_period='universal'` for the linear benchmark - a per-estimator methodology decision with a hand-calculated linear-violation gate | `diff_diff/pretrends.py` | 2(b) PR-1 R5 | Mid | Medium |
@@ -78,7 +78,6 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m
|-------|----------|--------|--------|----------|
| Committed `fixest::feols` event-study golden for TWFE `event_study=True` (within + pooled specs, unbalanced + covariate panels, matched CR1 cluster convention, per-period effects + vcov block) - the in-suite gates are shared-core cross-checks (TWFE-within == MPD-absorb, pooled == MPD bit-exact), so a defect common to the shared core would pass; the live-R harness (`benchmarks/R/benchmark_multiperiod.R`, `feols(y ~ treated * time_f \| unit)`) validated the within design in `docs/benchmarks.rst` but is not a committed regression test - follow the `fixest_did_twfe_golden.json` committed-golden pattern (pytest.skip when absent) | `tests/test_fixest_did_twfe_parity.py`, `benchmarks/R/` | 3(a) R2 | Mid | Medium |
| Type-blind `n_bootstrap` acceptance in already-validated estimators - HAD bool (`isinstance(..., int)` passes `True`, runs as 1 replicate), dCDH bool+float (its bare `< 0` check passes both `True` and `2.5`), TROP float (`2.5` passes the `>= 2` floor), SyntheticDiD float under all three variance methods + bool/negative under jackknife (its floor check is skipped there) - align these local checks with the `utils.validate_n_bootstrap` type guard (M-081 kept them out of the sweep: it scoped to previously-UNvalidated estimators only) | `diff_diff/had.py`, `diff_diff/chaisemartin_dhaultfoeuille.py`, `diff_diff/trop.py`, `diff_diff/synthetic_did.py` | 2(d) PR-B | Quick | Low |
-| Fit-time `aggregate=` teachings persist in the tutorials (M-020 family, removed at 4.0); migrate to post-fit `results.aggregate(...)`. The NARRATIVE-RST half is DONE (this row's original scope, re-scoped 2026-08-09): `choosing_estimator.rst`, `python_comparison.rst`, `r_comparison.rst` migrated; `troubleshooting.rst`'s bootstrap passage deliberately keeps fit-time `aggregate=` as the documented until-4.0 exception (post-fit recompute levels **raise `NotImplementedError` on a bootstrapped fit**, `staggered_results.py:323`). Remaining: **the notebook half** - executable code-cell sites across 9 notebooks (`02_staggered_did` 7, `09_real_world_examples` 6, `16_survey_did` 6, `26_composition_drift_calibration` 3, `21_had_pretest_workflow` 2, and one each in `08_triple_diff`, `16_wooldridge_etwfe`, `17_brand_awareness_survey`, `24_staggered_vs_collapsed_power`) - all nbmake-executed; `14_continuous_did`/`15_efficient_did` match only in markdown prose; bootstrapped fits keep the documented fit-time exception and 08's staggered DDD is canonical (M-140/M-141). API pages stay per their blockers: `docs/api/triple_diff.rst:56` (canonical DDD), `business_report.rst:77`/`diagnostic_report.rst:59` (report consumers read the raw `event_study_effects` field); `had.rst:164`/`continuous_did.rst:137` are prose-only and already correct | `docs/tutorials/*.ipynb`, `docs/api/*.rst` | 2(b) PR-4 | Heavy | Medium |
| Evaluate adding the `BaseEstimator` param surface (get_params/set_params) to the exported classes that never had it - `PowerAnalysis`, `LinearRegression`, `BusinessReport`, `DiagnosticReport`, `TWFEWeightsResult` (a NEW public surface, deliberately out of the 2(c)-i pure-refactor scope; `LinearRegression` is the one `fit`-bearing class excluded from the contract suite's roster-completeness test). | `diff_diff/linalg.py`, `diff_diff/power.py` | mixin PR | Mid | Low |
| Tighten the mypy suppressions that back the enforced-zero posture: burn down `prep_dgp`'s per-module `[index]` override (needs a None-vs-array restructure that preserves the seeded RNG stream), and evaluate re-enabling the globally disabled codes (`arg-type`, `return-value`, `var-annotated`, `assignment`) one at a time — `assignment` alone hid several real annotation drifts found during the 2026-07 triage. | `pyproject.toml` `[tool.mypy]`, `diff_diff/prep_dgp.py` | lint-CI | Mid | Low |
| MMM interop follow-up: Meridian `roi_calibration_period` mask builder - accept the MMM's time index + channel order and emit the boolean `(n_media_times, n_media_channels)` mask so `.to_code()` scopes the prior to the experiment window automatically (today the caller passes a mask expression / `full_model_window=True`). | `diff_diff/mmm.py` | mmm-interop | Quick | Low |
diff --git a/docs/tutorials/02_staggered_did.ipynb b/docs/tutorials/02_staggered_did.ipynb
index 0f43633a..ee410524 100644
--- a/docs/tutorials/02_staggered_did.ipynb
+++ b/docs/tutorials/02_staggered_did.ipynb
@@ -31,8 +31,15 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
+ "execution_count": 1,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:40:29.280981Z",
+ "iopub.status.busy": "2026-08-10T22:40:29.280582Z",
+ "iopub.status.idle": "2026-08-10T22:40:30.229004Z",
+ "shell.execute_reply": "2026-08-10T22:40:30.228503Z"
+ }
+ },
"outputs": [],
"source": [
"import numpy as np\n",
@@ -61,9 +68,177 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
+ "execution_count": 2,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:40:30.230171Z",
+ "iopub.status.busy": "2026-08-10T22:40:30.230074Z",
+ "iopub.status.idle": "2026-08-10T22:40:30.240045Z",
+ "shell.execute_reply": "2026-08-10T22:40:30.239670Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset: 800 observations, 100 units, 8 periods\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Visualization: Employment trends\n",
"if HAS_MATPLOTLIB:\n",
@@ -285,22 +825,215 @@
"cell_type": "markdown",
"id": "cell-14",
"metadata": {},
- "source": "---\n\n## Castle Doctrine Laws: Staggered Adoption\n\n### Background\n\nCastle Doctrine (or \"Stand Your Ground\") laws expand self-defense rights by removing the duty to retreat before using deadly force. These laws were adopted by different U.S. states at different times, creating a **staggered adoption** design.\n\n**Research question**: Do Castle Doctrine laws affect homicide rates?\n\n**Design**: Staggered DiD\n- **Treatment**: Adoption of Castle Doctrine law\n- **Cohorts**: States adopting in 2005, 2006, 2007, 2008, 2009\n- **Control**: States that never adopted during the study period\n\n**Key finding**: Cheng & Hoekstra (2013) found an approximately 8% increase in homicide rates following adoption.\n\n> **Specification note.** This is the authors' data, but not the authors' regression. Below we\n> use the binary `treated` indicator and the *level* homicide rate, to show what the modern\n> staggered-DiD estimators do. Cheng-Hoekstra regress the *log* rate on `treatment_exposure` -\n> `CDL_it`, the fraction of the year the law was in effect - which the loader also provides.\n> Treat the estimates here as an estimator demonstration, not a reproduction of their table."
+ "source": [
+ "---\n",
+ "\n",
+ "## Castle Doctrine Laws: Staggered Adoption\n",
+ "\n",
+ "### Background\n",
+ "\n",
+ "Castle Doctrine (or \"Stand Your Ground\") laws expand self-defense rights by removing the duty to retreat before using deadly force. These laws were adopted by different U.S. states at different times, creating a **staggered adoption** design.\n",
+ "\n",
+ "**Research question**: Do Castle Doctrine laws affect homicide rates?\n",
+ "\n",
+ "**Design**: Staggered DiD\n",
+ "- **Treatment**: Adoption of Castle Doctrine law\n",
+ "- **Cohorts**: States adopting in 2005, 2006, 2007, 2008, 2009\n",
+ "- **Control**: States that never adopted during the study period\n",
+ "\n",
+ "**Key finding**: Cheng & Hoekstra (2013) found an approximately 8% increase in homicide rates following adoption.\n",
+ "\n",
+ "> **Specification note.** This is the authors' data, but not the authors' regression. Below we\n",
+ "> use the binary `treated` indicator and the *level* homicide rate, to show what the modern\n",
+ "> staggered-DiD estimators do. Cheng-Hoekstra regress the *log* rate on `treatment_exposure` -\n",
+ "> `CDL_it`, the fraction of the year the law was in effect - which the loader also provides.\n",
+ "> Treat the estimates here as an estimator demonstration, not a reproduction of their table."
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "cell-15",
- "metadata": {},
- "outputs": [],
- "source": "# Load the Castle Doctrine dataset\ncastle = load_castle_doctrine()\n\nprint(f\"Dataset shape: {castle.shape}\")\nprint(f\"Provenance: {castle.attrs['source']}\")\nprint(f\" (cheng_hoekstra_castle_data = canonical; synthetic_fallback = generated, not a replication)\")\nprint(f\"Years: {castle['year'].min()} to {castle['year'].max()}\")\nprint(f\"States: {castle['state'].nunique()}\")\ncastle.head()"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:40:33.765501Z",
+ "iopub.status.busy": "2026-08-10T22:40:33.765425Z",
+ "iopub.status.idle": "2026-08-10T22:40:33.777035Z",
+ "shell.execute_reply": "2026-08-10T22:40:33.776756Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset shape: (550, 9)\n",
+ "Provenance: cheng_hoekstra_castle_data\n",
+ " (cheng_hoekstra_castle_data = canonical; synthetic_fallback = generated, not a replication)\n",
+ "Years: 2000 to 2010\n",
+ "States: 50\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Event study visualization\n",
"if HAS_MATPLOTLIB:\n",
@@ -654,16 +2147,53 @@
"cell_type": "markdown",
"id": "cell-35",
"metadata": {},
- "source": "### Dynamic Effects Pattern\n\nThe event study above traces effects by time since adoption: roughly flat before treatment, a jump at adoption, then decay. That shape is built into the simulated data, so recovering it shows the event-study aggregation working as intended. It is not evidence about divorce law, and the flat pre-period is not a parallel-trends test that any real design has passed.\n\nThe shape is loosely modelled on the \"spike and fade\" Wolfers (2006) reported using the actual state panel, where an initial rise in divorce rates diminished over time - attributed to unilateral divorce mostly bringing forward divorces that would have happened anyway. That finding stands on the real data, not on this simulation.\n\nWhat this section genuinely demonstrates is mechanical: how `CallawaySantAnna` reports effects relative to adoption on a long panel with many cohorts, and how to read an event study it produces."
+ "source": [
+ "### Dynamic Effects Pattern\n",
+ "\n",
+ "The event study above traces effects by time since adoption: roughly flat before treatment, a jump at adoption, then decay. That shape is built into the simulated data, so recovering it shows the event-study aggregation working as intended. It is not evidence about divorce law, and the flat pre-period is not a parallel-trends test that any real design has passed.\n",
+ "\n",
+ "The shape is loosely modelled on the \"spike and fade\" Wolfers (2006) reported using the actual state panel, where an initial rise in divorce rates diminished over time - attributed to unilateral divorce mostly bringing forward divorces that would have happened anyway. That finding stands on the real data, not on this simulation.\n",
+ "\n",
+ "What this section genuinely demonstrates is mechanical: how `CallawaySantAnna` reports effects relative to adoption on a long panel with many cohorts, and how to read an event study it produces."
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"id": "cell-36",
- "metadata": {},
- "outputs": [],
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:40:34.159893Z",
+ "iopub.status.busy": "2026-08-10T22:40:34.159815Z",
+ "iopub.status.idle": "2026-08-10T22:40:34.161708Z",
+ "shell.execute_reply": "2026-08-10T22:40:34.161370Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Effects by Adoption Cohort\n",
+ "==================================================\n",
+ "Cohort 1969: 0.0254 (SE: 0.1040) \n",
+ "Cohort 1970: 0.5027 (SE: 0.2189) *\n",
+ "Cohort 1971: 0.1403 (SE: 0.1228) \n",
+ "Cohort 1972: 0.2876 (SE: 0.1283) *\n",
+ "Cohort 1973: 0.0888 (SE: 0.0836) \n",
+ "Cohort 1974: 0.3658 (SE: 0.0680) *\n",
+ "Cohort 1975: 0.0037 (SE: 0.1374) \n",
+ "Cohort 1977: 0.7881 (SE: 0.1233) *\n",
+ "Cohort 1978: -0.1811 (SE: 0.0586) *\n",
+ "Cohort 1984: 0.4695 (SE: 0.1302) *\n",
+ "Cohort 1985: 0.4806 (SE: 0.0570) *\n",
+ "Cohort 1987: 0.2475 (SE: 0.1316) \n"
+ ]
+ }
+ ],
"source": [
- "# Effects by cohort (group_effects is populated when aggregate='group' or 'all')\n",
+ "# Effects by cohort (group_effects is populated by the fit-time aggregation -\n",
+ "# the documented route on this bootstrapped fit)\n",
"print(\"Effects by Adoption Cohort\")\n",
"print(\"=\" * 50)\n",
"\n",
@@ -677,14 +2207,82 @@
"cell_type": "markdown",
"id": "cell-37",
"metadata": {},
- "source": "---\n\n## Summary\n\n### Key Takeaways\n\n1. **Card-Krueger (1994)** - checksum-verified replication data\n - Classic 2x2 DiD design\n - Simple before/after, treatment/control comparison\n - The canonical survey is incomplete (26 store-waves lack an employment reading), so drop missing outcomes before fitting\n - Key insight: Minimum wage increases don't necessarily reduce employment\n\n2. **Castle Doctrine Laws** - checksum-verified replication data\n - Staggered adoption across states\n - TWFE can be biased; use CS or Sun-Abraham\n - Bacon decomposition reveals the problem with TWFE\n - Estimated here on the level rate with a binary indicator, not the paper's log-rate / fractional-exposure specification\n\n3. **Unilateral Divorce Laws** - **simulated panel**, `df.attrs[\"source\"] == \"synthetic_fallback\"`\n - Long panel with many cohorts\n - Event study reveals time-varying patterns\n - Demonstrates estimator mechanics only; the numbers are not evidence about divorce law\n\n### Checking provenance\n\nEvery loader marks where its data came from, and warns when it falls back to generated data:\n\n```python\ndf = load_castle_doctrine()\ndf.attrs[\"source\"] # 'cheng_hoekstra_castle_data' -> canonical\n # 'synthetic_fallback' -> generated, not a replication\n```\n\n### When to Use Which Estimator\n\n| Design | Recommended Estimator |\n|--------|----------------------|\n| Classic 2x2 | `DifferenceInDifferences` |\n| Panel with 2 periods | `DifferenceInDifferences` or `TwoWayFixedEffects` |\n| Staggered adoption | `CallawaySantAnna` or `SunAbraham` |\n| Heterogeneous timing | Always use `CallawaySantAnna` / `SunAbraham` |\n| Few never-treated | `CallawaySantAnna(control_group='not_yet_treated')` |\n\n### References\n\nThe divorce-law papers below are cited as context for the design the simulated panel imitates, not as results this notebook reproduces.\n\n- Card, D., & Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. *American Economic Review*, 84(4), 772-793.\n\n- Cheng, C., & Hoekstra, M. (2013). Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine. *Journal of Human Resources*, 48(3), 821-854.\n\n- Stevenson, B., & Wolfers, J. (2006). Bargaining in the Shadow of the Law: Divorce Laws and Family Distress. *Quarterly Journal of Economics*, 121(1), 267-288.\n\n- Wolfers, J. (2006). Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation and New Results. *American Economic Review*, 96(5), 1802-1820.\n\n- Callaway, B., & Sant'Anna, P. H. (2021). Difference-in-differences with multiple time periods. *Journal of Econometrics*, 225(2), 200-230.\n\n- Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. *Journal of Econometrics*, 225(2), 254-277."
+ "source": [
+ "---\n",
+ "\n",
+ "## Summary\n",
+ "\n",
+ "### Key Takeaways\n",
+ "\n",
+ "1. **Card-Krueger (1994)** - checksum-verified replication data\n",
+ " - Classic 2x2 DiD design\n",
+ " - Simple before/after, treatment/control comparison\n",
+ " - The canonical survey is incomplete (26 store-waves lack an employment reading), so drop missing outcomes before fitting\n",
+ " - Key insight: Minimum wage increases don't necessarily reduce employment\n",
+ "\n",
+ "2. **Castle Doctrine Laws** - checksum-verified replication data\n",
+ " - Staggered adoption across states\n",
+ " - TWFE can be biased; use CS or Sun-Abraham\n",
+ " - Bacon decomposition reveals the problem with TWFE\n",
+ " - Estimated here on the level rate with a binary indicator, not the paper's log-rate / fractional-exposure specification\n",
+ "\n",
+ "3. **Unilateral Divorce Laws** - **simulated panel**, `df.attrs[\"source\"] == \"synthetic_fallback\"`\n",
+ " - Long panel with many cohorts\n",
+ " - Event study reveals time-varying patterns\n",
+ " - Demonstrates estimator mechanics only; the numbers are not evidence about divorce law\n",
+ "\n",
+ "### Checking provenance\n",
+ "\n",
+ "Every loader marks where its data came from, and warns when it falls back to generated data:\n",
+ "\n",
+ "```python\n",
+ "df = load_castle_doctrine()\n",
+ "df.attrs[\"source\"] # 'cheng_hoekstra_castle_data' -> canonical\n",
+ " # 'synthetic_fallback' -> generated, not a replication\n",
+ "```\n",
+ "\n",
+ "### When to Use Which Estimator\n",
+ "\n",
+ "| Design | Recommended Estimator |\n",
+ "|--------|----------------------|\n",
+ "| Classic 2x2 | `DifferenceInDifferences` |\n",
+ "| Panel with 2 periods | `DifferenceInDifferences` or `TwoWayFixedEffects` |\n",
+ "| Staggered adoption | `CallawaySantAnna` or `SunAbraham` |\n",
+ "| Heterogeneous timing | Always use `CallawaySantAnna` / `SunAbraham` |\n",
+ "| Few never-treated | `CallawaySantAnna(control_group='not_yet_treated')` |\n",
+ "\n",
+ "### References\n",
+ "\n",
+ "The divorce-law papers below are cited as context for the design the simulated panel imitates, not as results this notebook reproduces.\n",
+ "\n",
+ "- Card, D., & Krueger, A. B. (1994). Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania. *American Economic Review*, 84(4), 772-793.\n",
+ "\n",
+ "- Cheng, C., & Hoekstra, M. (2013). Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine. *Journal of Human Resources*, 48(3), 821-854.\n",
+ "\n",
+ "- Stevenson, B., & Wolfers, J. (2006). Bargaining in the Shadow of the Law: Divorce Laws and Family Distress. *Quarterly Journal of Economics*, 121(1), 267-288.\n",
+ "\n",
+ "- Wolfers, J. (2006). Did Unilateral Divorce Laws Raise Divorce Rates? A Reconciliation and New Results. *American Economic Review*, 96(5), 1802-1820.\n",
+ "\n",
+ "- Callaway, B., & Sant'Anna, P. H. (2021). Difference-in-differences with multiple time periods. *Journal of Econometrics*, 225(2), 200-230.\n",
+ "\n",
+ "- Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. *Journal of Econometrics*, 225(2), 254-277."
+ ]
}
],
"metadata": {
"language_info": {
- "name": "python"
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.14.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/docs/tutorials/16_survey_did.ipynb b/docs/tutorials/16_survey_did.ipynb
index 552b16d9..8b543a04 100644
--- a/docs/tutorials/16_survey_did.ipynb
+++ b/docs/tutorials/16_survey_did.ipynb
@@ -30,14 +30,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "cell-01-97f332d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-04-06T15:24:53.381899Z",
- "iopub.status.busy": "2026-04-06T15:24:53.381705Z",
- "iopub.status.idle": "2026-04-06T15:24:54.236834Z",
- "shell.execute_reply": "2026-04-06T15:24:54.236445Z"
+ "iopub.execute_input": "2026-08-10T22:40:34.918614Z",
+ "iopub.status.busy": "2026-08-10T22:40:34.918463Z",
+ "iopub.status.idle": "2026-08-10T22:40:35.627412Z",
+ "shell.execute_reply": "2026-08-10T22:40:35.626908Z"
}
},
"outputs": [],
@@ -82,17 +82,243 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "cell-03-d90fe7b7",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-04-06T15:24:54.238375Z",
- "iopub.status.busy": "2026-04-06T15:24:54.238260Z",
- "iopub.status.idle": "2026-04-06T15:24:54.256354Z",
- "shell.execute_reply": "2026-04-06T15:24:54.256133Z"
+ "iopub.execute_input": "2026-08-10T22:40:35.628544Z",
+ "iopub.status.busy": "2026-08-10T22:40:35.628450Z",
+ "iopub.status.idle": "2026-08-10T22:40:35.642153Z",
+ "shell.execute_reply": "2026-08-10T22:40:35.641721Z"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset: 1600 observations, 200 respondents, 8 periods\n",
+ "\n",
+ "Survey structure:\n",
+ " Strata (regions): 5\n",
+ " PSUs (census tracts): 40\n",
+ " Weight range: [0.0, 3.9]\n",
+ " FPC per stratum: 200\n",
+ "\n",
+ "Treatment cohorts:\n",
+ "first_treat\n",
+ "0 60\n",
+ "3 70\n",
+ "5 70\n",
+ "Name: count, dtype: int64\n",
+ "\n",
+ "Ground truth (known from the data-generating process):\n",
+ " Population ATT: 2.504\n",
+ " Stratum TEs: 0.59, 1.29, 2.00, 2.71, 3.41\n",
+ " ICC (realized): 0.839\n",
+ " DEFF (Kish): 1.390\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
"
]
@@ -362,9 +362,13 @@
"# placebo contrasts instead — see the CallawaySantAnna docs.)\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\")\n",
- " es = CallawaySantAnna(control_group=\"never_treated\", base_period=\"universal\").fit(\n",
+ " res_cs = CallawaySantAnna(control_group=\"never_treated\", base_period=\"universal\").fit(\n",
" panel, outcome=\"outcome\", unit=\"unit\", time=\"period\",\n",
- " first_treat=\"first_treat\", aggregate=\"event_study\").event_study_effects\n",
+ " first_treat=\"first_treat\")\n",
+ " es_surface = res_cs.aggregate(\"event_study\")\n",
+ "# Key the columnar container by event time (same shape the legacy dict had)\n",
+ "es = {int(t): {\"effect\": a, \"se\": s}\n",
+ " for t, a, s in zip(es_surface.event_time, es_surface.att, es_surface.se)}\n",
"ev = np.array(sorted(k for k in es if -4 <= k <= 9))\n",
"eff = np.array([es[k][\"effect\"] for k in ev])\n",
"serr = np.array([es[k][\"se\"] for k in ev])\n",
@@ -395,10 +399,10 @@
"id": "cd8bb89e",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-31T17:34:23.643160Z",
- "iopub.status.busy": "2026-05-31T17:34:23.643090Z",
- "iopub.status.idle": "2026-05-31T17:34:32.168026Z",
- "shell.execute_reply": "2026-05-31T17:34:32.167759Z"
+ "iopub.execute_input": "2026-08-10T22:40:45.243918Z",
+ "iopub.status.busy": "2026-08-10T22:40:45.243857Z",
+ "iopub.status.idle": "2026-08-10T22:40:51.159747Z",
+ "shell.execute_reply": "2026-08-10T22:40:51.159322Z"
}
},
"outputs": [
@@ -537,10 +541,10 @@
"id": "823fbfb7",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-31T17:34:32.169035Z",
- "iopub.status.busy": "2026-05-31T17:34:32.168955Z",
- "iopub.status.idle": "2026-05-31T17:34:35.071481Z",
- "shell.execute_reply": "2026-05-31T17:34:35.071164Z"
+ "iopub.execute_input": "2026-08-10T22:40:51.160706Z",
+ "iopub.status.busy": "2026-08-10T22:40:51.160635Z",
+ "iopub.status.idle": "2026-08-10T22:40:53.156527Z",
+ "shell.execute_reply": "2026-08-10T22:40:53.156172Z"
}
},
"outputs": [
@@ -573,16 +577,16 @@
"id": "5f8e0c4b",
"metadata": {
"execution": {
- "iopub.execute_input": "2026-05-31T17:34:35.072605Z",
- "iopub.status.busy": "2026-05-31T17:34:35.072530Z",
- "iopub.status.idle": "2026-05-31T17:35:26.085255Z",
- "shell.execute_reply": "2026-05-31T17:35:26.084962Z"
+ "iopub.execute_input": "2026-08-10T22:40:53.157604Z",
+ "iopub.status.busy": "2026-08-10T22:40:53.157541Z",
+ "iopub.status.idle": "2026-08-10T22:41:27.624583Z",
+ "shell.execute_reply": "2026-08-10T22:41:27.624220Z"
}
},
"outputs": [
{
"data": {
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",
+ "image/png": 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",
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@@ -756,10 +760,10 @@
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- "iopub.status.busy": "2026-05-31T17:35:26.086255Z",
- "iopub.status.idle": "2026-05-31T17:35:26.950218Z",
- "shell.execute_reply": "2026-05-31T17:35:26.949945Z"
+ "iopub.execute_input": "2026-08-10T22:41:27.625613Z",
+ "iopub.status.busy": "2026-08-10T22:41:27.625554Z",
+ "iopub.status.idle": "2026-08-10T22:41:28.227131Z",
+ "shell.execute_reply": "2026-08-10T22:41:28.226735Z"
}
},
"outputs": [
@@ -803,10 +807,10 @@
"id": "34cfb285",
"metadata": {
"execution": {
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- "shell.execute_reply": "2026-05-31T17:35:45.176708Z"
+ "iopub.execute_input": "2026-08-10T22:41:28.228106Z",
+ "iopub.status.busy": "2026-08-10T22:41:28.228036Z",
+ "iopub.status.idle": "2026-08-10T22:41:40.367042Z",
+ "shell.execute_reply": "2026-08-10T22:41:40.366647Z"
}
},
"outputs": [
@@ -924,7 +928,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.6"
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diff --git a/docs/tutorials/26_composition_drift_calibration.ipynb b/docs/tutorials/26_composition_drift_calibration.ipynb
index 3824e355..258987ac 100644
--- a/docs/tutorials/26_composition_drift_calibration.ipynb
+++ b/docs/tutorials/26_composition_drift_calibration.ipynb
@@ -4,223 +4,1244 @@
"cell_type": "markdown",
"id": "cell-00",
"metadata": {},
- "source": "# When Who Answers Changes: Survey Calibration for Causal DiD\n\nSurvey samples change composition over time: response rates decline, and they decline unevenly across demographic groups. This tutorial shows when that drift breaks a difference-in-differences estimate itself - not just descriptive statistics - and how to fix it by pairing diff-diff with Meta's [balance](https://import-balance.org/) calibration package.\n\n**A matched pair.** This notebook is the diff-diff-side companion to balance's [`balance_diff_diff_brfss` tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb). Their lesson is reassurance: when non-response drift is *common* to treated and control units, it badly biases descriptive trends (which raking repairs) but largely **differences out** of the DiD. This notebook is the warning that completes the pair: when the drift is *differential* - correlated with treatment timing - the DiD is biased too, pre-trend tests won't catch it, and calibration becomes essential for the **causal** estimand. Along the way we hit a subtlety their setting never triggers: raking *granularity*.\n\n**This tutorial covers:**\n\n1. A BRFSS-style staggered smoking-ban dataset with no arm-specific trends by construction - population parallel trends hold in expectation (planted ATT -3.0pp, realized -2.98pp after a rarely-binding probability floor) - but differentially drifting non-response\n2. Why treatment-correlated composition drift does **not** difference out of a DiD - with clean pre-trends the whole way\n3. A per-wave *national* rake as a false fix, and raking at the granularity of your comparison units (state-year, as BRFSS itself does) as the real one\n4. The diff-diff/balance seam both ways: the native `SurveyDesign` + `aggregate_survey` three-liner, and the `balance.interop.diff_diff` adapter (`to_panel_for_did` / `fit_did`) - with an exact-parity check\n5. An estimator sweep (CallawaySantAnna / SunAbraham / ImputationDiD): composition bias is a data problem, not an estimator problem\n6. Cross-package diagnostics: `survey_metadata` design effects and `as_balance_diagnostic`\n7. A practical checklist: when calibration matters for causal vs descriptive estimands\n\n**Prerequisites:** [Tutorial 01: Basic DiD](01_basic_did.ipynb), [Tutorial 02: Staggered DiD](02_staggered_did.ipynb), [Tutorial 16: Survey DiD](16_survey_did.ipynb); ideally the [balance quickstart](https://import-balance.org/docs/tutorials/quickstart/).\n\n**Requirements:** `pip install diff-diff \"balance>=0.21\" matplotlib`. balance is needed **only for this tutorial** - it is not a diff-diff dependency. (`pip install \"balance[did]\"` installs both packages at once.)\n"
+ "source": [
+ "# When Who Answers Changes: Survey Calibration for Causal DiD\n",
+ "\n",
+ "Survey samples change composition over time: response rates decline, and they decline unevenly across demographic groups. This tutorial shows when that drift breaks a difference-in-differences estimate itself - not just descriptive statistics - and how to fix it by pairing diff-diff with Meta's [balance](https://import-balance.org/) calibration package.\n",
+ "\n",
+ "**A matched pair.** This notebook is the diff-diff-side companion to balance's [`balance_diff_diff_brfss` tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb). Their lesson is reassurance: when non-response drift is *common* to treated and control units, it badly biases descriptive trends (which raking repairs) but largely **differences out** of the DiD. This notebook is the warning that completes the pair: when the drift is *differential* - correlated with treatment timing - the DiD is biased too, pre-trend tests won't catch it, and calibration becomes essential for the **causal** estimand. Along the way we hit a subtlety their setting never triggers: raking *granularity*.\n",
+ "\n",
+ "**This tutorial covers:**\n",
+ "\n",
+ "1. A BRFSS-style staggered smoking-ban dataset with no arm-specific trends by construction - population parallel trends hold in expectation (planted ATT -3.0pp, realized -2.98pp after a rarely-binding probability floor) - but differentially drifting non-response\n",
+ "2. Why treatment-correlated composition drift does **not** difference out of a DiD - with clean pre-trends the whole way\n",
+ "3. A per-wave *national* rake as a false fix, and raking at the granularity of your comparison units (state-year, as BRFSS itself does) as the real one\n",
+ "4. The diff-diff/balance seam both ways: the native `SurveyDesign` + `aggregate_survey` three-liner, and the `balance.interop.diff_diff` adapter (`to_panel_for_did` / `fit_did`) - with an exact-parity check\n",
+ "5. An estimator sweep (CallawaySantAnna / SunAbraham / ImputationDiD): composition bias is a data problem, not an estimator problem\n",
+ "6. Cross-package diagnostics: `survey_metadata` design effects and `as_balance_diagnostic`\n",
+ "7. A practical checklist: when calibration matters for causal vs descriptive estimands\n",
+ "\n",
+ "**Prerequisites:** [Tutorial 01: Basic DiD](01_basic_did.ipynb), [Tutorial 02: Staggered DiD](02_staggered_did.ipynb), [Tutorial 16: Survey DiD](16_survey_did.ipynb); ideally the [balance quickstart](https://import-balance.org/docs/tutorials/quickstart/).\n",
+ "\n",
+ "**Requirements:** `pip install diff-diff \"balance>=0.21\" matplotlib`. balance is needed **only for this tutorial** - it is not a diff-diff dependency. (`pip install \"balance[did]\"` installs both packages at once.)\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-01",
"metadata": {},
- "source": "## Two regimes of non-response drift\n\n\"Do the survey weights matter?\" is really two questions - one per estimand:\n\n| Non-response drift | Descriptive estimand (a prevalence trend) | Causal DiD estimand (an ATT) |\n|---|---|---|\n| **Common** - same in treated and control units | **Biased** - raking to population margins repairs it | **Robust** - composition changes difference out ([balance's tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb)) |\n| **Differential** - correlated with treatment x time | Biased | **Biased** - the drift *is* treatment x post shaped, so the DiD reads it as treatment effect (**this tutorial**) |\n\nThe dangerous cell is the bottom-right one, because nothing in the standard DiD toolkit flags it: the fit converges, standard errors look fine, and - as we will see - the pre-treatment event-study coefficients stay clean.\n"
+ "source": [
+ "## Two regimes of non-response drift\n",
+ "\n",
+ "\"Do the survey weights matter?\" is really two questions - one per estimand:\n",
+ "\n",
+ "| Non-response drift | Descriptive estimand (a prevalence trend) | Causal DiD estimand (an ATT) |\n",
+ "|---|---|---|\n",
+ "| **Common** - same in treated and control units | **Biased** - raking to population margins repairs it | **Robust** - composition changes difference out ([balance's tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb)) |\n",
+ "| **Differential** - correlated with treatment x time | Biased | **Biased** - the drift *is* treatment x post shaped, so the DiD reads it as treatment effect (**this tutorial**) |\n",
+ "\n",
+ "The dangerous cell is the bottom-right one, because nothing in the standard DiD toolkit flags it: the fit converges, standard errors look fine, and - as we will see - the pre-treatment event-study coefficients stay clean.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "cell-02",
- "metadata": {},
- "outputs": [],
- "source": "import logging\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\nimport balance # >= 0.21 for balance.interop.diff_diff\nfrom balance import Sample\nfrom balance.interop import diff_diff as bd\n\n# Quiet balance's per-call INFO logging (must run AFTER `import balance` -\n# its __init__ re-arms the logger) and pandas FutureWarnings.\nlogging.getLogger(\"balance\").setLevel(logging.ERROR)\nwarnings.filterwarnings(\"ignore\", category=FutureWarning)\n# diff-diff normalizes pweights to mean 1 and warns to say so; expected here.\nwarnings.filterwarnings(\n \"ignore\", message=\".*weights normalized.*\", category=UserWarning\n)\n\nimport diff_diff\nfrom diff_diff import CallawaySantAnna, SurveyDesign, aggregate_survey\n\ntry:\n import matplotlib.pyplot as plt\n\n plt.style.use(\"seaborn-v0_8-whitegrid\")\n HAS_MATPLOTLIB = True\nexcept ImportError:\n HAS_MATPLOTLIB = False\n\nprint(f\"diff-diff {diff_diff.__version__} | balance {balance.__version__}\")\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:41.124841Z",
+ "iopub.status.busy": "2026-08-10T22:41:41.124398Z",
+ "iopub.status.idle": "2026-08-10T22:41:42.417972Z",
+ "shell.execute_reply": "2026-08-10T22:41:42.417629Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO (2026-08-10 18:41:42,373) [__init__/ (line 77)]: Using balance version 0.23.0\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO (2026-08-10 18:41:42,373) [__init__/ (line 82)]: \n",
+ "balance (Version 0.23.0) loaded:\n",
+ " 📖 Documentation: https://import-balance.org/\n",
+ " 🛠️ Help / Issues: https://github.com/facebookresearch/balance/issues/\n",
+ " 📄 Citation:\n",
+ " Sarig, T., Galili, T., & Eilat, R. (2023).\n",
+ " balance - a Python package for balancing biased data samples.\n",
+ " https://arxiv.org/abs/2307.06024\n",
+ "\n",
+ " Tip: You can view this message anytime with balance.help()\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "diff-diff 3.9.0 | balance 0.23.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "import logging\n",
+ "import warnings\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "import balance # >= 0.21 for balance.interop.diff_diff\n",
+ "from balance import Sample\n",
+ "from balance.interop import diff_diff as bd\n",
+ "\n",
+ "# Quiet balance's per-call INFO logging (must run AFTER `import balance` -\n",
+ "# its __init__ re-arms the logger) and pandas FutureWarnings.\n",
+ "logging.getLogger(\"balance\").setLevel(logging.ERROR)\n",
+ "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+ "# diff-diff normalizes pweights to mean 1 and warns to say so; expected here.\n",
+ "warnings.filterwarnings(\n",
+ " \"ignore\", message=\".*weights normalized.*\", category=UserWarning\n",
+ ")\n",
+ "\n",
+ "import diff_diff\n",
+ "from diff_diff import CallawaySantAnna, SurveyDesign, aggregate_survey\n",
+ "\n",
+ "try:\n",
+ " import matplotlib.pyplot as plt\n",
+ "\n",
+ " plt.style.use(\"seaborn-v0_8-whitegrid\")\n",
+ " HAS_MATPLOTLIB = True\n",
+ "except ImportError:\n",
+ " HAS_MATPLOTLIB = False\n",
+ "\n",
+ "print(f\"diff-diff {diff_diff.__version__} | balance {balance.__version__}\")\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-03",
"metadata": {},
- "source": "## The data: a BRFSS-shaped survey around staggered smoking bans\n\nWe simulate respondent-level microdata mirroring the public-use [BRFSS](https://www.cdc.gov/brfss/annual_data/annual_2024.html) file: 50 states x 7 years (2018-2024), with 1,200 adults invited per state-year, of whom roughly 580-860 respond (~265,000 respondent rows in total). Comprehensive indoor-smoking bans take effect in 10 states in 2020 and 10 more in 2022; 30 states never adopt. The outcome is **current smoking** (binary `smoker`). Each respondent carries raking demographics (`age_band`, `educ_cat`), a complex design (`stratum`, `psu`, `fpc`), and a demographic-blind `design_weight` standing in for BRFSS `_LLCPWT`. A separate 20,000-row \"ACS\" frame supplies the population margins (and per-state adult population counts) we will rake to.\n\nThe generator is built so every headline claim is checkable against ground truth:\n\n- **No arm-specific trends by construction.** Smoking prevalence is additive and linear in education, age, and time; demographic margins are identical in every state and year. Trends differ *by demographic* (smoking falls fastest among the college-educated) but identically across treatment arms, so population parallel trends hold **in expectation**; mean-zero PSU-year shocks (kept in deliberately - they create the design effects the diagnostics section reads) add seed-specific noise but no systematic drift.\n- **The planted treatment effect is -3.0pp** in every treated state-year. One honest wrinkle: probabilities are floored at 1%, and that floor binds for ~2% of treated-post person-years (elderly college-educated respondents in low-smoking states), so the **realized population ATT is -2.98pp**. The generator computes it from the clipped potential outcomes and returns it as `truth[\"realized_att_pp\"]` - that is the truth line we hold every estimate against below.\n- **Non-response depends only on the raking observables** (`age_band`, `educ_cat`) - never on smoking itself. Response rates decline over time everywhere, concentrated among younger and less-educated adults (the real BRFSS pattern). That is the *common* drift.\n- **The differential twist** (`differential=True`): after a state's ban takes effect, response among `hs_or_less` adults falls an extra 7pp per event year - enforcement publicity raises refusals in the low-SES communities where smoking is concentrated, and the implementation consumes the follow-up-call budget. Scenario A (`differential=False`) and scenario B share the identical invited population and outcomes; they differ **only in who answers the phone**.\n\nBecause non-response is driven entirely by variables inside the raking margins (missing-at-random given the margins), calibration *can* fix everything - the interesting question is what kind of calibration. To run this on real data, replace this one cell with `pyreadstat.read_xport(...)` calls per BRFSS year.\n"
+ "source": [
+ "## The data: a BRFSS-shaped survey around staggered smoking bans\n",
+ "\n",
+ "We simulate respondent-level microdata mirroring the public-use [BRFSS](https://www.cdc.gov/brfss/annual_data/annual_2024.html) file: 50 states x 7 years (2018-2024), with 1,200 adults invited per state-year, of whom roughly 580-860 respond (~265,000 respondent rows in total). Comprehensive indoor-smoking bans take effect in 10 states in 2020 and 10 more in 2022; 30 states never adopt. The outcome is **current smoking** (binary `smoker`). Each respondent carries raking demographics (`age_band`, `educ_cat`), a complex design (`stratum`, `psu`, `fpc`), and a demographic-blind `design_weight` standing in for BRFSS `_LLCPWT`. A separate 20,000-row \"ACS\" frame supplies the population margins (and per-state adult population counts) we will rake to.\n",
+ "\n",
+ "The generator is built so every headline claim is checkable against ground truth:\n",
+ "\n",
+ "- **No arm-specific trends by construction.** Smoking prevalence is additive and linear in education, age, and time; demographic margins are identical in every state and year. Trends differ *by demographic* (smoking falls fastest among the college-educated) but identically across treatment arms, so population parallel trends hold **in expectation**; mean-zero PSU-year shocks (kept in deliberately - they create the design effects the diagnostics section reads) add seed-specific noise but no systematic drift.\n",
+ "- **The planted treatment effect is -3.0pp** in every treated state-year. One honest wrinkle: probabilities are floored at 1%, and that floor binds for ~2% of treated-post person-years (elderly college-educated respondents in low-smoking states), so the **realized population ATT is -2.98pp**. The generator computes it from the clipped potential outcomes and returns it as `truth[\"realized_att_pp\"]` - that is the truth line we hold every estimate against below.\n",
+ "- **Non-response depends only on the raking observables** (`age_band`, `educ_cat`) - never on smoking itself. Response rates decline over time everywhere, concentrated among younger and less-educated adults (the real BRFSS pattern). That is the *common* drift.\n",
+ "- **The differential twist** (`differential=True`): after a state's ban takes effect, response among `hs_or_less` adults falls an extra 7pp per event year - enforcement publicity raises refusals in the low-SES communities where smoking is concentrated, and the implementation consumes the follow-up-call budget. Scenario A (`differential=False`) and scenario B share the identical invited population and outcomes; they differ **only in who answers the phone**.\n",
+ "\n",
+ "Because non-response is driven entirely by variables inside the raking margins (missing-at-random given the margins), calibration *can* fix everything - the interesting question is what kind of calibration. To run this on real data, replace this one cell with `pyreadstat.read_xport(...)` calls per BRFSS year.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "cell-04",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:42.419061Z",
+ "iopub.status.busy": "2026-08-10T22:41:42.418954Z",
+ "iopub.status.idle": "2026-08-10T22:41:42.424440Z",
+ "shell.execute_reply": "2026-08-10T22:41:42.424104Z"
+ }
+ },
"outputs": [],
- "source": "N_STATES = 50\nYEARS = np.arange(2018, 2025)\nN_INVITED = 1200\nN_STRATA = 5\nPSUS_PER_STATE = 8\nFPC_PSUS_PER_STRATUM = 200.0\n\nAGE_BANDS = [\"18-34\", \"35-49\", \"50-64\", \"65+\"]\nAGE_SHARES = np.array([0.30, 0.25, 0.25, 0.20])\nEDUC_CATS = [\"hs_or_less\", \"some_college\", \"college_plus\"]\nEDUC_SHARES = np.array([0.35, 0.30, 0.35])\n\nBASE_EDUC_PP = np.array([22.0, 15.0, 9.0])\nAGE_ADJ_PP = np.array([2.0, 3.0, 1.0, -2.0])\nTREND_COMMON_PP = 0.25\nTREND_EDUC_PP = np.array([-0.15, 0.0, 0.10])\nTRUE_ATT_PP = -3.0\nSTATE_RE_SD_PP = 1.5\nPSU_SHOCK_SD_PP = 0.8\nP_CLIP_PP = (1.0, 60.0)\n\nR_BASE = 0.70\nR_AGE_SHIFT = np.array([-0.10, -0.02, 0.03, 0.08])\nR_EDUC_SHIFT = np.array([-0.09, 0.00, 0.06])\nR_COMMON_DRIFT_EDUC = np.array([0.015, 0.0075, 0.0])\nR_COMMON_DRIFT_YOUNG = 0.010\nR_DIFF_DRIFT_PER_EVENT_YEAR = 0.07\nR_CLIP = (0.10, 0.95)\n\nTARGET_N = 20_000\nSEED = 20260704\n\n\ndef simulate_brfss_smoking(differential, seed=SEED, drift_start_offset=0):\n \"\"\"BRFSS-style microdata around staggered smoking bans.\n\n No arm-specific trends by construction: population parallel trends\n hold in expectation (mean-zero PSU-year shocks add noise, not drift),\n and the planted effect is -3.0pp (realized population ATT ~-2.98pp\n after the probability floor). All SYSTEMATIC estimator bias in this\n notebook comes from sample composition. Scenarios A/B share invited\n respondents and outcomes for the same seed - they differ only in who\n responds.\n \"\"\"\n rng = np.random.default_rng(seed)\n\n perm = rng.permutation(N_STATES)\n g_of_state = np.zeros(N_STATES, dtype=int)\n g_of_state[perm[:10]] = 2020\n g_of_state[perm[10:20]] = 2022\n stratum_of_state = rng.integers(0, N_STRATA, size=N_STATES)\n state_pop = rng.lognormal(mean=np.log(4e6), sigma=0.6, size=N_STATES)\n state_re = np.clip(rng.normal(0.0, STATE_RE_SD_PP, size=N_STATES), -3.0, 3.0)\n psu_shock = rng.normal(\n 0.0, PSU_SHOCK_SD_PP, size=(N_STATES, PSUS_PER_STATE, len(YEARS))\n )\n\n n_inv = N_STATES * len(YEARS) * N_INVITED\n state = np.repeat(np.arange(N_STATES), len(YEARS) * N_INVITED)\n year = np.tile(np.repeat(YEARS, N_INVITED), N_STATES)\n age_idx = rng.choice(len(AGE_BANDS), size=n_inv, p=AGE_SHARES)\n educ_idx = rng.choice(len(EDUC_CATS), size=n_inv, p=EDUC_SHARES)\n psu_idx = rng.integers(0, PSUS_PER_STATE, size=n_inv)\n u_respond = rng.uniform(size=n_inv)\n u_smoker = rng.uniform(size=n_inv)\n weight_jitter = rng.uniform(0.85, 1.15, size=n_inv)\n\n k = year - YEARS[0]\n year_idx = year - YEARS[0]\n g = g_of_state[state]\n treated_post = (g > 0) & (year >= g)\n\n base_pp = (\n BASE_EDUC_PP[educ_idx]\n + AGE_ADJ_PP[age_idx]\n + state_re[state]\n + psu_shock[state, psu_idx, year_idx]\n - (TREND_COMMON_PP + TREND_EDUC_PP[educ_idx]) * k\n )\n p_pp = np.clip(base_pp + TRUE_ATT_PP * treated_post, *P_CLIP_PP)\n smoker = (u_smoker < p_pp / 100.0).astype(int)\n\n r = (\n R_BASE\n + R_AGE_SHIFT[age_idx]\n + R_EDUC_SHIFT[educ_idx]\n - R_COMMON_DRIFT_EDUC[educ_idx] * k\n - R_COMMON_DRIFT_YOUNG * k * (age_idx == 0)\n )\n if differential:\n event_time = year - g - drift_start_offset\n hit = (g > 0) & (event_time >= 0) & (educ_idx == 0)\n r = r - R_DIFF_DRIFT_PER_EVENT_YEAR * (event_time + 1) * hit\n r = np.clip(r, *R_CLIP)\n responded = u_respond < r\n\n micro = pd.DataFrame(\n {\n \"id\": np.arange(n_inv)[responded],\n \"state\": state[responded],\n \"year\": year[responded],\n \"g\": g[responded],\n \"smoker\": smoker[responded],\n \"age_band\": np.array(AGE_BANDS)[age_idx[responded]],\n \"educ_cat\": np.array(EDUC_CATS)[educ_idx[responded]],\n \"stratum\": stratum_of_state[state[responded]],\n \"psu\": state[responded] * 100 + psu_idx[responded],\n \"fpc\": FPC_PSUS_PER_STRATUM,\n \"design_weight\": (state_pop[state] / N_INVITED * weight_jitter)[\n responded\n ],\n }\n )\n\n rng_t = np.random.default_rng(seed + 1)\n target_df = pd.DataFrame(\n {\n \"id\": np.arange(TARGET_N),\n \"age_band\": np.array(AGE_BANDS)[\n rng_t.choice(len(AGE_BANDS), size=TARGET_N, p=AGE_SHARES)\n ],\n \"educ_cat\": np.array(EDUC_CATS)[\n rng_t.choice(len(EDUC_CATS), size=TARGET_N, p=EDUC_SHARES)\n ],\n }\n )\n\n kk = YEARS - YEARS[0]\n cell = (\n BASE_EDUC_PP[None, :, None]\n + AGE_ADJ_PP[None, None, :]\n - (TREND_COMMON_PP + TREND_EDUC_PP[None, :, None]) * kk[:, None, None]\n )\n tp = (g_of_state[None, :] > 0) & (YEARS[:, None] >= g_of_state[None, :])\n base_prev = np.einsum(\"tea,e,a->t\", cell, EDUC_SHARES, AGE_SHARES)\n w_s = state_pop / state_pop.sum()\n pop_prev = base_prev + TRUE_ATT_PP * (tp * w_s[None, :]).sum(axis=1)\n # Realized population ATT: the probability floor P_CLIP_PP[0] binds for\n # ~2% of treated-post person-years, attenuating the planted -3.0pp.\n y1 = np.clip(base_pp + TRUE_ATT_PP, *P_CLIP_PP)\n y0 = np.clip(base_pp, *P_CLIP_PP)\n w_pop = state_pop[state]\n realized_att_pp = ((y1 - y0) * w_pop)[treated_post].sum() / w_pop[\n treated_post\n ].sum()\n truth = {\n \"true_att_pp\": TRUE_ATT_PP,\n \"realized_att_pp\": float(realized_att_pp),\n \"floor_bind_share\": float(\n ((y1 - y0) > TRUE_ATT_PP + 1e-12)[treated_post].mean()\n ),\n \"pop_prevalence_by_year\": dict(zip(YEARS.tolist(), pop_prev / 100.0)),\n \"g_of_state\": g_of_state,\n \"state_pop\": state_pop,\n }\n return micro, target_df, truth\n"
+ "source": [
+ "N_STATES = 50\n",
+ "YEARS = np.arange(2018, 2025)\n",
+ "N_INVITED = 1200\n",
+ "N_STRATA = 5\n",
+ "PSUS_PER_STATE = 8\n",
+ "FPC_PSUS_PER_STRATUM = 200.0\n",
+ "\n",
+ "AGE_BANDS = [\"18-34\", \"35-49\", \"50-64\", \"65+\"]\n",
+ "AGE_SHARES = np.array([0.30, 0.25, 0.25, 0.20])\n",
+ "EDUC_CATS = [\"hs_or_less\", \"some_college\", \"college_plus\"]\n",
+ "EDUC_SHARES = np.array([0.35, 0.30, 0.35])\n",
+ "\n",
+ "BASE_EDUC_PP = np.array([22.0, 15.0, 9.0])\n",
+ "AGE_ADJ_PP = np.array([2.0, 3.0, 1.0, -2.0])\n",
+ "TREND_COMMON_PP = 0.25\n",
+ "TREND_EDUC_PP = np.array([-0.15, 0.0, 0.10])\n",
+ "TRUE_ATT_PP = -3.0\n",
+ "STATE_RE_SD_PP = 1.5\n",
+ "PSU_SHOCK_SD_PP = 0.8\n",
+ "P_CLIP_PP = (1.0, 60.0)\n",
+ "\n",
+ "R_BASE = 0.70\n",
+ "R_AGE_SHIFT = np.array([-0.10, -0.02, 0.03, 0.08])\n",
+ "R_EDUC_SHIFT = np.array([-0.09, 0.00, 0.06])\n",
+ "R_COMMON_DRIFT_EDUC = np.array([0.015, 0.0075, 0.0])\n",
+ "R_COMMON_DRIFT_YOUNG = 0.010\n",
+ "R_DIFF_DRIFT_PER_EVENT_YEAR = 0.07\n",
+ "R_CLIP = (0.10, 0.95)\n",
+ "\n",
+ "TARGET_N = 20_000\n",
+ "SEED = 20260704\n",
+ "\n",
+ "\n",
+ "def simulate_brfss_smoking(differential, seed=SEED, drift_start_offset=0):\n",
+ " \"\"\"BRFSS-style microdata around staggered smoking bans.\n",
+ "\n",
+ " No arm-specific trends by construction: population parallel trends\n",
+ " hold in expectation (mean-zero PSU-year shocks add noise, not drift),\n",
+ " and the planted effect is -3.0pp (realized population ATT ~-2.98pp\n",
+ " after the probability floor). All SYSTEMATIC estimator bias in this\n",
+ " notebook comes from sample composition. Scenarios A/B share invited\n",
+ " respondents and outcomes for the same seed - they differ only in who\n",
+ " responds.\n",
+ " \"\"\"\n",
+ " rng = np.random.default_rng(seed)\n",
+ "\n",
+ " perm = rng.permutation(N_STATES)\n",
+ " g_of_state = np.zeros(N_STATES, dtype=int)\n",
+ " g_of_state[perm[:10]] = 2020\n",
+ " g_of_state[perm[10:20]] = 2022\n",
+ " stratum_of_state = rng.integers(0, N_STRATA, size=N_STATES)\n",
+ " state_pop = rng.lognormal(mean=np.log(4e6), sigma=0.6, size=N_STATES)\n",
+ " state_re = np.clip(rng.normal(0.0, STATE_RE_SD_PP, size=N_STATES), -3.0, 3.0)\n",
+ " psu_shock = rng.normal(\n",
+ " 0.0, PSU_SHOCK_SD_PP, size=(N_STATES, PSUS_PER_STATE, len(YEARS))\n",
+ " )\n",
+ "\n",
+ " n_inv = N_STATES * len(YEARS) * N_INVITED\n",
+ " state = np.repeat(np.arange(N_STATES), len(YEARS) * N_INVITED)\n",
+ " year = np.tile(np.repeat(YEARS, N_INVITED), N_STATES)\n",
+ " age_idx = rng.choice(len(AGE_BANDS), size=n_inv, p=AGE_SHARES)\n",
+ " educ_idx = rng.choice(len(EDUC_CATS), size=n_inv, p=EDUC_SHARES)\n",
+ " psu_idx = rng.integers(0, PSUS_PER_STATE, size=n_inv)\n",
+ " u_respond = rng.uniform(size=n_inv)\n",
+ " u_smoker = rng.uniform(size=n_inv)\n",
+ " weight_jitter = rng.uniform(0.85, 1.15, size=n_inv)\n",
+ "\n",
+ " k = year - YEARS[0]\n",
+ " year_idx = year - YEARS[0]\n",
+ " g = g_of_state[state]\n",
+ " treated_post = (g > 0) & (year >= g)\n",
+ "\n",
+ " base_pp = (\n",
+ " BASE_EDUC_PP[educ_idx]\n",
+ " + AGE_ADJ_PP[age_idx]\n",
+ " + state_re[state]\n",
+ " + psu_shock[state, psu_idx, year_idx]\n",
+ " - (TREND_COMMON_PP + TREND_EDUC_PP[educ_idx]) * k\n",
+ " )\n",
+ " p_pp = np.clip(base_pp + TRUE_ATT_PP * treated_post, *P_CLIP_PP)\n",
+ " smoker = (u_smoker < p_pp / 100.0).astype(int)\n",
+ "\n",
+ " r = (\n",
+ " R_BASE\n",
+ " + R_AGE_SHIFT[age_idx]\n",
+ " + R_EDUC_SHIFT[educ_idx]\n",
+ " - R_COMMON_DRIFT_EDUC[educ_idx] * k\n",
+ " - R_COMMON_DRIFT_YOUNG * k * (age_idx == 0)\n",
+ " )\n",
+ " if differential:\n",
+ " event_time = year - g - drift_start_offset\n",
+ " hit = (g > 0) & (event_time >= 0) & (educ_idx == 0)\n",
+ " r = r - R_DIFF_DRIFT_PER_EVENT_YEAR * (event_time + 1) * hit\n",
+ " r = np.clip(r, *R_CLIP)\n",
+ " responded = u_respond < r\n",
+ "\n",
+ " micro = pd.DataFrame(\n",
+ " {\n",
+ " \"id\": np.arange(n_inv)[responded],\n",
+ " \"state\": state[responded],\n",
+ " \"year\": year[responded],\n",
+ " \"g\": g[responded],\n",
+ " \"smoker\": smoker[responded],\n",
+ " \"age_band\": np.array(AGE_BANDS)[age_idx[responded]],\n",
+ " \"educ_cat\": np.array(EDUC_CATS)[educ_idx[responded]],\n",
+ " \"stratum\": stratum_of_state[state[responded]],\n",
+ " \"psu\": state[responded] * 100 + psu_idx[responded],\n",
+ " \"fpc\": FPC_PSUS_PER_STRATUM,\n",
+ " \"design_weight\": (state_pop[state] / N_INVITED * weight_jitter)[\n",
+ " responded\n",
+ " ],\n",
+ " }\n",
+ " )\n",
+ "\n",
+ " rng_t = np.random.default_rng(seed + 1)\n",
+ " target_df = pd.DataFrame(\n",
+ " {\n",
+ " \"id\": np.arange(TARGET_N),\n",
+ " \"age_band\": np.array(AGE_BANDS)[\n",
+ " rng_t.choice(len(AGE_BANDS), size=TARGET_N, p=AGE_SHARES)\n",
+ " ],\n",
+ " \"educ_cat\": np.array(EDUC_CATS)[\n",
+ " rng_t.choice(len(EDUC_CATS), size=TARGET_N, p=EDUC_SHARES)\n",
+ " ],\n",
+ " }\n",
+ " )\n",
+ "\n",
+ " kk = YEARS - YEARS[0]\n",
+ " cell = (\n",
+ " BASE_EDUC_PP[None, :, None]\n",
+ " + AGE_ADJ_PP[None, None, :]\n",
+ " - (TREND_COMMON_PP + TREND_EDUC_PP[None, :, None]) * kk[:, None, None]\n",
+ " )\n",
+ " tp = (g_of_state[None, :] > 0) & (YEARS[:, None] >= g_of_state[None, :])\n",
+ " base_prev = np.einsum(\"tea,e,a->t\", cell, EDUC_SHARES, AGE_SHARES)\n",
+ " w_s = state_pop / state_pop.sum()\n",
+ " pop_prev = base_prev + TRUE_ATT_PP * (tp * w_s[None, :]).sum(axis=1)\n",
+ " # Realized population ATT: the probability floor P_CLIP_PP[0] binds for\n",
+ " # ~2% of treated-post person-years, attenuating the planted -3.0pp.\n",
+ " y1 = np.clip(base_pp + TRUE_ATT_PP, *P_CLIP_PP)\n",
+ " y0 = np.clip(base_pp, *P_CLIP_PP)\n",
+ " w_pop = state_pop[state]\n",
+ " realized_att_pp = ((y1 - y0) * w_pop)[treated_post].sum() / w_pop[\n",
+ " treated_post\n",
+ " ].sum()\n",
+ " truth = {\n",
+ " \"true_att_pp\": TRUE_ATT_PP,\n",
+ " \"realized_att_pp\": float(realized_att_pp),\n",
+ " \"floor_bind_share\": float(\n",
+ " ((y1 - y0) > TRUE_ATT_PP + 1e-12)[treated_post].mean()\n",
+ " ),\n",
+ " \"pop_prevalence_by_year\": dict(zip(YEARS.tolist(), pop_prev / 100.0)),\n",
+ " \"g_of_state\": g_of_state,\n",
+ " \"state_pop\": state_pop,\n",
+ " }\n",
+ " return micro, target_df, truth\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "cell-05",
- "metadata": {},
- "outputs": [],
- "source": "micro_common, target, truth = simulate_brfss_smoking(differential=False)\nmicro_diff, _, _ = simulate_brfss_smoking(differential=True)\n\nby_cell = micro_diff.groupby([\"state\", \"year\"]).size()\nprint(f\"scenario A respondents: {len(micro_common):,}\")\nprint(f\"scenario B respondents: {len(micro_diff):,}\")\nprint(f\"respondents per state-year (B): {by_cell.min()}-{by_cell.max()}\")\nTRUE_ATT = truth[\"realized_att_pp\"]\nprint(f\"planted ATT: {truth['true_att_pp']}pp | realized population ATT: \"\n f\"{TRUE_ATT:+.2f}pp (probability floor binds for \"\n f\"{truth['floor_bind_share']:.1%} of treated-post person-years)\")\ntarget.head(3)\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:42.425377Z",
+ "iopub.status.busy": "2026-08-10T22:41:42.425314Z",
+ "iopub.status.idle": "2026-08-10T22:41:42.551473Z",
+ "shell.execute_reply": "2026-08-10T22:41:42.551109Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "scenario A respondents: 271,569\n",
+ "scenario B respondents: 265,368\n",
+ "respondents per state-year (B): 581-856\n",
+ "planted ATT: -3.0pp | realized population ATT: -2.98pp (probability floor binds for 2.0% of treated-post person-years)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
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+ " \n",
+ "
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+ "
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+ "
id
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+ "
age_band
\n",
+ "
educ_cat
\n",
+ "
\n",
+ " \n",
+ " \n",
+ "
\n",
+ "
0
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+ "
0
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+ "
65+
\n",
+ "
college_plus
\n",
+ "
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+ "
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+ "
1
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+ "
1
\n",
+ "
50-64
\n",
+ "
hs_or_less
\n",
+ "
\n",
+ "
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+ "
2
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+ "
2
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+ "
50-64
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+ "
college_plus
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id age_band educ_cat\n",
+ "0 0 65+ college_plus\n",
+ "1 1 50-64 hs_or_less\n",
+ "2 2 50-64 college_plus"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "micro_common, target, truth = simulate_brfss_smoking(differential=False)\n",
+ "micro_diff, _, _ = simulate_brfss_smoking(differential=True)\n",
+ "\n",
+ "by_cell = micro_diff.groupby([\"state\", \"year\"]).size()\n",
+ "print(f\"scenario A respondents: {len(micro_common):,}\")\n",
+ "print(f\"scenario B respondents: {len(micro_diff):,}\")\n",
+ "print(f\"respondents per state-year (B): {by_cell.min()}-{by_cell.max()}\")\n",
+ "TRUE_ATT = truth[\"realized_att_pp\"]\n",
+ "print(f\"planted ATT: {truth['true_att_pp']}pp | realized population ATT: \"\n",
+ " f\"{TRUE_ATT:+.2f}pp (probability floor binds for \"\n",
+ " f\"{truth['floor_bind_share']:.1%} of treated-post person-years)\")\n",
+ "target.head(3)\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-06",
"metadata": {},
- "source": "## The native seam: `SurveyDesign` + `aggregate_survey` + `CallawaySantAnna`\n\ndiff-diff's survey path consumes calibrated weights as *just a column*. The handoff from microdata to a modern staggered estimator is three calls:\n\n1. **`SurveyDesign(weights=..., strata=..., psu=..., fpc=...)`** - declare the complex design on the microdata (column *names*, no copies).\n2. **`aggregate_survey(micro, by=[\"state\", \"year\"], outcomes=\"smoker\", ...)`** - collapse respondents to a state-year panel of design-weighted prevalences with full Taylor-linearized precision tracking, returning the panel *and* a pre-configured second-stage `SurveyDesign` (population weights + state-level clustering, taken from the first `by` column).\n3. **`CallawaySantAnna(...).fit(panel, ..., survey_design=second_stage)`** - the staggered DiD with design-based variance.\n\nOne convention worth noticing now: we named the design columns `stratum` / `psu` / `fpc`. Those are exactly the defaults in `balance.interop.conventions.DEFAULT_DESIGN_COLUMNS`, so when we reach the balance adapter below it will wire the same design automatically.\n\nWe wrap the three calls in a helper because we are about to fit the same model under four different weight columns - that swap being a one-argument change is the point of the design.\n"
+ "source": [
+ "## The native seam: `SurveyDesign` + `aggregate_survey` + `CallawaySantAnna`\n",
+ "\n",
+ "diff-diff's survey path consumes calibrated weights as *just a column*. The handoff from microdata to a modern staggered estimator is three calls:\n",
+ "\n",
+ "1. **`SurveyDesign(weights=..., strata=..., psu=..., fpc=...)`** - declare the complex design on the microdata (column *names*, no copies).\n",
+ "2. **`aggregate_survey(micro, by=[\"state\", \"year\"], outcomes=\"smoker\", ...)`** - collapse respondents to a state-year panel of design-weighted prevalences with full Taylor-linearized precision tracking, returning the panel *and* a pre-configured second-stage `SurveyDesign` (population weights + state-level clustering, taken from the first `by` column).\n",
+ "3. **`CallawaySantAnna(...).fit(panel, ..., survey_design=second_stage)`** - the staggered DiD with design-based variance.\n",
+ "\n",
+ "One convention worth noticing now: we named the design columns `stratum` / `psu` / `fpc`. Those are exactly the defaults in `balance.interop.conventions.DEFAULT_DESIGN_COLUMNS`, so when we reach the balance adapter below it will wire the same design automatically.\n",
+ "\n",
+ "We wrap the three calls in a helper because we are about to fit the same model under four different weight columns - that swap being a one-argument change is the point of the design.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "cell-07",
- "metadata": {},
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:42.552416Z",
+ "iopub.status.busy": "2026-08-10T22:41:42.552347Z",
+ "iopub.status.idle": "2026-08-10T22:41:42.554631Z",
+ "shell.execute_reply": "2026-08-10T22:41:42.554282Z"
+ }
+ },
"outputs": [],
- "source": "def fit_survey_cs(micro, weights_col):\n \"\"\"Native seam: microdata + a weight column -> survey-weighted CS fit.\"\"\"\n design = SurveyDesign(\n weights=weights_col, strata=\"stratum\", psu=\"psu\", fpc=\"fpc\"\n )\n panel, second_stage = aggregate_survey(\n micro, by=[\"state\", \"year\"], outcomes=\"smoker\", survey_design=design\n )\n panel = panel.merge(\n micro[[\"state\", \"g\"]].drop_duplicates(), on=\"state\", how=\"left\"\n )\n cs = CallawaySantAnna(\n estimation_method=\"reg\",\n control_group=\"not_yet_treated\",\n base_period=\"universal\",\n )\n return cs.fit(\n panel,\n outcome=\"smoker_mean\",\n unit=\"state\",\n time=\"year\",\n first_treat=\"g\",\n survey_design=second_stage,\n aggregate=\"all\",\n )\n\n\ndef show_att(res, label):\n lo, hi = res.overall_conf_int\n print(\n f\"{label}: ATT = {res.overall_att * 100:+.2f}pp \"\n f\"(SE {res.overall_se * 100:.2f}, 95% CI [{lo * 100:+.2f}, {hi * 100:+.2f}])\"\n )\n"
+ "source": [
+ "def fit_survey_cs(micro, weights_col):\n",
+ " \"\"\"Native seam: microdata + a weight column -> survey-weighted CS fit.\"\"\"\n",
+ " design = SurveyDesign(\n",
+ " weights=weights_col, strata=\"stratum\", psu=\"psu\", fpc=\"fpc\"\n",
+ " )\n",
+ " panel, second_stage = aggregate_survey(\n",
+ " micro, by=[\"state\", \"year\"], outcomes=\"smoker\", survey_design=design\n",
+ " )\n",
+ " panel = panel.merge(\n",
+ " micro[[\"state\", \"g\"]].drop_duplicates(), on=\"state\", how=\"left\"\n",
+ " )\n",
+ " cs = CallawaySantAnna(\n",
+ " estimation_method=\"reg\",\n",
+ " control_group=\"not_yet_treated\",\n",
+ " base_period=\"universal\",\n",
+ " )\n",
+ " return cs.fit(\n",
+ " panel,\n",
+ " outcome=\"smoker_mean\",\n",
+ " unit=\"state\",\n",
+ " time=\"year\",\n",
+ " first_treat=\"g\",\n",
+ " survey_design=second_stage,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def show_att(res, label):\n",
+ " lo, hi = res.overall_conf_int\n",
+ " print(\n",
+ " f\"{label}: ATT = {res.overall_att * 100:+.2f}pp \"\n",
+ " f\"(SE {res.overall_se * 100:.2f}, 95% CI [{lo * 100:+.2f}, {hi * 100:+.2f}])\"\n",
+ " )\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-08",
"metadata": {},
- "source": "## Scenario A - common drift: the descriptive trend breaks, the DiD does not\n\nFirst the regime the balance tutorial covers. Response rates decline over time, concentrated among younger and less-educated adults, but *identically* in treated and control states. The design-weighted national prevalence drifts away from the truth (the sample is increasingly educated, and educated adults smoke less - so the naive trend overstates the decline). Yet the DiD contrast is untouched, because both arms mis-measure levels the same way.\n"
+ "source": [
+ "## Scenario A - common drift: the descriptive trend breaks, the DiD does not\n",
+ "\n",
+ "First the regime the balance tutorial covers. Response rates decline over time, concentrated among younger and less-educated adults, but *identically* in treated and control states. The design-weighted national prevalence drifts away from the truth (the sample is increasingly educated, and educated adults smoke less - so the naive trend overstates the decline). Yet the DiD contrast is untouched, because both arms mis-measure levels the same way.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "cell-09",
- "metadata": {},
- "outputs": [],
- "source": "res_a = fit_survey_cs(micro_common, \"design_weight\")\nshow_att(res_a, \"Scenario A, design weights\")\n\npop_series = pd.Series(truth[\"pop_prevalence_by_year\"])\ndesign_series = micro_common.groupby(\"year\")[[\"smoker\", \"design_weight\"]].apply(\n lambda d: (d.smoker * d.design_weight).sum() / d.design_weight.sum()\n)\ngap_pp = (design_series - pop_series) * 100\nprint(f\"descriptive gap, design-weighted vs population: \"\n f\"{gap_pp.loc[2018]:+.2f}pp (2018) -> {gap_pp.loc[2024]:+.2f}pp (2024)\")\n\nif HAS_MATPLOTLIB:\n fig, ax = plt.subplots(figsize=(8, 4.5))\n ax.plot(pop_series.index, pop_series * 100, \"k-\", lw=2, label=\"population (truth)\")\n ax.plot(design_series.index, design_series * 100, \"C1--\", lw=2, marker=\"o\",\n label=\"design-weighted sample\")\n ax.set_xlabel(\"year\")\n ax.set_ylabel(\"smoking prevalence (%)\")\n ax.set_title(\"Scenario A: common drift biases the descriptive trend\")\n ax.legend()\n plt.tight_layout()\n plt.show()\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:42.555467Z",
+ "iopub.status.busy": "2026-08-10T22:41:42.555413Z",
+ "iopub.status.idle": "2026-08-10T22:41:42.899148Z",
+ "shell.execute_reply": "2026-08-10T22:41:42.898743Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Scenario A, design weights: ATT = -3.09pp (SE 0.40, 95% CI [-3.90, -2.28])\n",
+ "descriptive gap, design-weighted vs population: -0.29pp (2018) -> -1.04pp (2024)\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "res_a = fit_survey_cs(micro_common, \"design_weight\")\n",
+ "show_att(res_a, \"Scenario A, design weights\")\n",
+ "\n",
+ "pop_series = pd.Series(truth[\"pop_prevalence_by_year\"])\n",
+ "design_series = micro_common.groupby(\"year\")[[\"smoker\", \"design_weight\"]].apply(\n",
+ " lambda d: (d.smoker * d.design_weight).sum() / d.design_weight.sum()\n",
+ ")\n",
+ "gap_pp = (design_series - pop_series) * 100\n",
+ "print(f\"descriptive gap, design-weighted vs population: \"\n",
+ " f\"{gap_pp.loc[2018]:+.2f}pp (2018) -> {gap_pp.loc[2024]:+.2f}pp (2024)\")\n",
+ "\n",
+ "if HAS_MATPLOTLIB:\n",
+ " fig, ax = plt.subplots(figsize=(8, 4.5))\n",
+ " ax.plot(pop_series.index, pop_series * 100, \"k-\", lw=2, label=\"population (truth)\")\n",
+ " ax.plot(design_series.index, design_series * 100, \"C1--\", lw=2, marker=\"o\",\n",
+ " label=\"design-weighted sample\")\n",
+ " ax.set_xlabel(\"year\")\n",
+ " ax.set_ylabel(\"smoking prevalence (%)\")\n",
+ " ax.set_title(\"Scenario A: common drift biases the descriptive trend\")\n",
+ " ax.legend()\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-10",
"metadata": {},
- "source": "The ATT comes back at about **-3.1pp against the -2.98pp realized truth** - the causal estimate is robust even though the descriptive series visibly drifts below the truth. This is exactly the reassurance half of the story; for the full descriptive-repair workflow (per-wave raking, Love plots, effective sample sizes) see the [balance tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb) - we will not repeat it here.\n\nNow we break it.\n"
+ "source": [
+ "The ATT comes back at about **-3.1pp against the -2.98pp realized truth** - the causal estimate is robust even though the descriptive series visibly drifts below the truth. This is exactly the reassurance half of the story; for the full descriptive-repair workflow (per-wave raking, Love plots, effective sample sizes) see the [balance tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb) - we will not repeat it here.\n",
+ "\n",
+ "Now we break it.\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-11",
"metadata": {},
- "source": "## Scenario B - differential drift: the causal estimand breaks\n\nSame states, same bans, same outcomes, same common drift - plus one mechanism: once a ban takes effect, response among `hs_or_less` adults in that state falls an extra 7pp per event year. The composition damage is easy to see if you know to look at it:\n"
+ "source": [
+ "## Scenario B - differential drift: the causal estimand breaks\n",
+ "\n",
+ "Same states, same bans, same outcomes, same common drift - plus one mechanism: once a ban takes effect, response among `hs_or_less` adults in that state falls an extra 7pp per event year. The composition damage is easy to see if you know to look at it:\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "cell-12",
- "metadata": {},
- "outputs": [],
- "source": "hs_share = (\n micro_diff.assign(hs=lambda d: (d.educ_cat == \"hs_or_less\").astype(float))\n .groupby([\"year\", \"g\"])[[\"hs\", \"design_weight\"]]\n .apply(lambda d: (d.hs * d.design_weight).sum() / d.design_weight.sum())\n .unstack()\n)\nprint(\"design-weighted hs_or_less share by cohort (population share: 0.350):\")\nprint(hs_share.round(3).to_string())\n\nif HAS_MATPLOTLIB:\n fig, ax = plt.subplots(figsize=(8, 4.5))\n styles = {0: (\"C0\", \"never treated (30 states)\"),\n 2020: (\"C3\", \"ban in 2020 (10 states)\"),\n 2022: (\"C1\", \"ban in 2022 (10 states)\")}\n for gval, (color, label) in styles.items():\n ax.plot(hs_share.index, hs_share[gval], color=color, marker=\"o\", label=label)\n ax.axhline(0.35, color=\"gray\", ls=\":\", label=\"population share (0.35)\")\n for gval, color in [(2020, \"C3\"), (2022, \"C1\")]:\n ax.axvline(gval, color=color, ls=\"--\", alpha=0.4)\n ax.set_xlabel(\"year\")\n ax.set_ylabel(\"hs_or_less share of design-weighted sample\")\n ax.set_title(\"Scenario B: who answers changes exactly when treatment starts\")\n ax.legend()\n plt.tight_layout()\n plt.show()\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:42.900039Z",
+ "iopub.status.busy": "2026-08-10T22:41:42.899967Z",
+ "iopub.status.idle": "2026-08-10T22:41:42.975754Z",
+ "shell.execute_reply": "2026-08-10T22:41:42.975405Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "design-weighted hs_or_less share by cohort (population share: 0.350):\n",
+ "g 0 2020 2022\n",
+ "year \n",
+ "2018 0.310 0.304 0.310\n",
+ "2019 0.304 0.306 0.317\n",
+ "2020 0.301 0.276 0.300\n",
+ "2021 0.292 0.242 0.297\n",
+ "2022 0.288 0.191 0.264\n",
+ "2023 0.286 0.142 0.215\n",
+ "2024 0.281 0.111 0.187\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "hs_share = (\n",
+ " micro_diff.assign(hs=lambda d: (d.educ_cat == \"hs_or_less\").astype(float))\n",
+ " .groupby([\"year\", \"g\"])[[\"hs\", \"design_weight\"]]\n",
+ " .apply(lambda d: (d.hs * d.design_weight).sum() / d.design_weight.sum())\n",
+ " .unstack()\n",
+ ")\n",
+ "print(\"design-weighted hs_or_less share by cohort (population share: 0.350):\")\n",
+ "print(hs_share.round(3).to_string())\n",
+ "\n",
+ "if HAS_MATPLOTLIB:\n",
+ " fig, ax = plt.subplots(figsize=(8, 4.5))\n",
+ " styles = {0: (\"C0\", \"never treated (30 states)\"),\n",
+ " 2020: (\"C3\", \"ban in 2020 (10 states)\"),\n",
+ " 2022: (\"C1\", \"ban in 2022 (10 states)\")}\n",
+ " for gval, (color, label) in styles.items():\n",
+ " ax.plot(hs_share.index, hs_share[gval], color=color, marker=\"o\", label=label)\n",
+ " ax.axhline(0.35, color=\"gray\", ls=\":\", label=\"population share (0.35)\")\n",
+ " for gval, color in [(2020, \"C3\"), (2022, \"C1\")]:\n",
+ " ax.axvline(gval, color=color, ls=\"--\", alpha=0.4)\n",
+ " ax.set_xlabel(\"year\")\n",
+ " ax.set_ylabel(\"hs_or_less share of design-weighted sample\")\n",
+ " ax.set_title(\"Scenario B: who answers changes exactly when treatment starts\")\n",
+ " ax.legend()\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-13",
"metadata": {},
- "source": "Each cohort's low-education share peels away from ~0.30 **exactly at its adoption year**, sliding to ~0.11 (2020 cohort) and ~0.19 (2022 cohort) by 2024 while never-treated states stay flat.\n\nWhy this bias cannot difference out: the design weights are demographic-blind, so a state-year's measured prevalence is $m(s,t) = \\sum_e \\sigma_e(s,t)\\, p_e(t)$, where $\\sigma_e$ is the *realized sample* share of education group $e$ and $p_e$ its true prevalence. A DiD passes any additive term that is common across arms - that killed the composition term in scenario A. But here $\\sigma_{hs}$ falls **only in treated states, only after adoption**: the composition artifact $\\Delta\\sigma_{hs} \\times (p_{hs} - \\bar{p}_{rest})$ sits precisely in the treatment x post cell of the design. To the estimator, it *is* an ATT. With a ~12pp gap in smoking prevalence between `hs_or_less` and the (share-weighted) rest, a 17pp share collapse manufactures roughly an extra -2pp of \"effect\" at long horizons, ramping up from ~-0.3pp at adoption - which masquerades as a treatment effect that *grows over time*.\n"
+ "source": [
+ "Each cohort's low-education share peels away from ~0.30 **exactly at its adoption year**, sliding to ~0.11 (2020 cohort) and ~0.19 (2022 cohort) by 2024 while never-treated states stay flat.\n",
+ "\n",
+ "Why this bias cannot difference out: the design weights are demographic-blind, so a state-year's measured prevalence is $m(s,t) = \\sum_e \\sigma_e(s,t)\\, p_e(t)$, where $\\sigma_e$ is the *realized sample* share of education group $e$ and $p_e$ its true prevalence. A DiD passes any additive term that is common across arms - that killed the composition term in scenario A. But here $\\sigma_{hs}$ falls **only in treated states, only after adoption**: the composition artifact $\\Delta\\sigma_{hs} \\times (p_{hs} - \\bar{p}_{rest})$ sits precisely in the treatment x post cell of the design. To the estimator, it *is* an ATT. With a ~12pp gap in smoking prevalence between `hs_or_less` and the (share-weighted) rest, a 17pp share collapse manufactures roughly an extra -2pp of \"effect\" at long horizons, ramping up from ~-0.3pp at adoption - which masquerades as a treatment effect that *grows over time*.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "cell-14",
- "metadata": {},
- "outputs": [],
- "source": "res_design = fit_survey_cs(micro_diff, \"design_weight\")\nshow_att(res_design, \"Scenario B, design weights\")\nprint(f\"true ATT (realized): {TRUE_ATT:+.2f}pp\")\n\nes_design = res_design.event_study_effects\nif HAS_MATPLOTLIB:\n fig, ax = plt.subplots(figsize=(8, 4.5))\n ev = sorted(es_design)\n eff = np.array([es_design[e][\"effect\"] for e in ev]) * 100\n se = np.array([es_design[e][\"se\"] for e in ev]) * 100\n ax.errorbar(ev, eff, yerr=1.96 * np.nan_to_num(se), fmt=\"o-\", color=\"C1\",\n capsize=3, label=\"design weights\")\n ax.axhline(0, color=\"gray\", lw=0.8)\n ax.axhline(TRUE_ATT, color=\"k\", ls=\":\", label=f\"true ATT ({TRUE_ATT:.2f}pp)\")\n ax.axvline(-0.5, color=\"gray\", ls=\"--\", alpha=0.5)\n ax.set_xlabel(\"event time (years since ban)\")\n ax.set_ylabel(\"effect on smoking prevalence (pp)\")\n ax.set_title(\"Scenario B, design weights: clean pre-trends, biased ATT\")\n ax.legend()\n plt.tight_layout()\n plt.show()\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:42.976740Z",
+ "iopub.status.busy": "2026-08-10T22:41:42.976668Z",
+ "iopub.status.idle": "2026-08-10T22:41:43.294071Z",
+ "shell.execute_reply": "2026-08-10T22:41:43.293642Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Scenario B, design weights: ATT = -4.13pp (SE 0.39, 95% CI [-4.92, -3.34])\n",
+ "true ATT (realized): -2.98pp\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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frgzgkx87+fGT5nXpr/v666+bB0K+8cYb6vaQIUPU1Wk5+ZCTSTkZ0fOWy0Bw6X40cuRIdbInV6/lKpb0qZYTcVvIAMSJEyeq+QLkx1Wubkq53nrrLUyZMkX1M7Z1XIOQ2cPl/2UwpFzBkxM9acmQuQ5Sm4RJuktZ9iGWH3rpcy6DMSXw0icOlFlnpS+z9HXWT6CtkfqROpQ88nLSJj/0cuIm9WO5TWp1KCcMcoIwbtw4dcIqVwil25oMLE+OPJ+MlZGTGb1LSI8ePdQJizO6LEiLxMaNG9X4Cp0cH3JC1bp16wS57dNynDir/LI/5WRLrrjK1VWZA8OW+rR1HgtbPieJ2VIfzvxMJNe9b8OGDeo15eRVui9JPaU2YNmRz7y1fZEer2PPZ1ACY1uOB1s/8/r7lMH7cgzI94+0Jsj3ruVgeXu//2wpoy11Zst3UuJB21Im6f5ojTyXlOOnn35SXbRq165t454hMjYvSQ3l6kIQuRP5QZETaWnJkCtOyXVTkKBDmvLlRzG5vs1yAi7byBVJ/eq1LigoSJ2Qy4BxnfzgSTYd6Z6lD46Uj7C0osiPqt4lQPrwyw+kNOfL9vIDZ/k8+vPLj1viAYh6uWVcReIyWSOvnfhKsP56iWcilz71clIkk+6lNnmaXtcSoEg9y8mmXAGU92TZ9SGlOtTrR/ppSxmlTIn7OidXD7K9lFdaAeRKqJygSHc46W5k676xRq46y/6R96SXV+pc+ozLyallVhxb3qOzyy/r9DE3Uv8yPkaOLX1davUpryndg+R1bQlkUvqcSHlkXeJByrbs85Q+E2ndf3IRQbq1SJ99OfGVcugT41mW2drnzdayJyfxvnDkdex5/6l9Bm05Hmz5zMtry/EinwF5n/K9mjigsHasp7avbSmjLXVma33o5PMsZbA22F8nx5A8l+XnXt6LvEaJEiWS/T97tiPKaAwsiCjDyFVpOTmTq3mpZapyBfmhlqwy0kIl/avlJEYGaMoASzkxkZSWjnaHSk/uXn6jswwsyDM+80TkXBxjQUQZRjKySFcgo55gyJVM6ZqzYsUKNRhXuibJIleIpSuR0U/K3b385HmM/pknIudiiwURZRjp9mI5KZeRyZV+udoqJ0TJdZ0wMncvvxFZ60JEnvOZJyLHMbAgIiIiIiKHsSsUERERERE5jIEFERERERE5LIun9OGU/sSSltDWGUqJiIiIiChlcXFxKlW6pHVObcyURwQWElTIbJpEREREROR8ZcqUSTXDm0cEFvqkSvKGnTEzblrIxDqnT59GYGBgsrPGEusyo/G4zDx1KZOAyVUlIS23Rk4ta/S6dCesS9alEfG49Ky6lMkl5QJ+cpP9elxgoXd/kqAipRlv03vHC3l9/lCyLo2Cx2XmqUsp39atW9XtFi1aGLKM7lKX7oR1ybo0Ih6XnlmXtgw34IAEIiIiIiJyGAMLIiIiIiJyGAMLIiIiIiJyGAMLIiIiIiJyGAMLIiIiIiJyGAMLIiIiIiJymEekmyUiyuxk3opChQqZbxMREWU0BhZERB5A8otXq1bN1cUgIqJMjF2hiIiIiIjIYQwsiIiIiIjIYewKRUTkAWJjY7F161Z1u0WLFvDx8XF1kYjIQwQFBeH999/HN998k+GvvXfvXuzatQv9+vVD3rx51brZs2fj/v37yf7PK6+8gu+//z7VbaQLqTXHjx9HSEgIGjdunOz/h4eHY926dbhx4wbKli2LNm3aJPt84u7du9i4cSPu3LmD+vXro2bNmgkej4yMxOrVq3Hz5k1UrlwZzZs3h7Ps3LkTefLkyZDusmyxICIiIqJknTx5Ev/8849LauiTTz7Bzz//jD///NPq43Jyv2zZshSfw5ZtdA8ePMCoUaNQqlSpZLcJDg5G586d8ffff+PWrVv46KOP0L9/f0RHR1vd/syZM2r733//XQUiw4cPV+9Ld+/ePTz22GNYtGiRer6xY8finXfegbPIexk5cmSKgZazsMWCiIiIiKy6du2aOimXk9KvvvoKffv2VVf04+LiEBMTg0uXLqFjx47q5DksLAxdunQx/++cOXPUlfwKFSqo+1FRUVizZo1qAalYsSJatWqVYha7s2fP4sSJExgxYgR++uknPPfcc2r9oEGDzNtcuXJFtQK89tprCf7Xlm2smTt3LurUqYOAgIBkt/n6669Ro0YNzJgxQ90fMmQIOnTogBUrVuDxxx9Psv3HH3+snlP+T7z00kuqzmSRlou//vpL1YO8R2lt7tWrFzp16oQXX3wRZcqUwYEDB1QLSb58+VTrgwQK7du3R5Ys2mn85s2b1f9Ji461x4sXL65aSX744Qf12umJLRZkDKHBwNWD2hJ0CL53T6u/5nXyOBEREbnc9u3bMXr0aHUiLK0ZJpNJdcVctWpVkpP0f//9V92WE+OePXuq1ofbt29j2rRpeP7551VwkpzFixejSZMm6mr+1atX1UlzepLAR96TvF5KJKiQsuv8/PxQokQJFYRZI3UgAZZOAoDAwEDVNUpIkCaLTg8IpIurOHjwoGoVef3111WLx2effYaBAweaH5fAYurUqck+Lrp166bem7zH9MQWCzKGfXOBzVPUTekZXlVuaN3FNa1GAm1Guap0RERE6UpOvEXOnDnNV/HlJFC618iJZvbs2ZNs6+vra+7XL9vJ9nLlOkeOHClua48iRYqok9Jt27YluOIvrydjLixfKyUy5kH6+c+bN0+9Pylr9+7d8dtvv6FPnz5JtpeAQ7oaSZcgOXFv3bo1FixYoAKN9CJjOYS0LqREAiRL0mojAdaIESOsbi8tCNLqoP+ftOxIsCHBiHjyySdVYCbdqaQFY8OGDRg8eLAKPvTg4PLlyypwK1q0KF599VXVIrFy5Uq1b/QypPR43bp1VfCye/duNQ4vvbDFgoyh/gBg8Gbg+YdXO2L7r9DWySKPExEReajcuXOrRQbvWnahkXVyomipcOHCav3FixfN66Rbjqx74YUXEmwrXWlkvXQpciYZsGxrUKFfVZcAafr06apL1bfffqv+f9++fVa337RpkwqK5ARZSCvC+vXrk20VcAYZRyIDp+2ZZDQiIgJvvvmm6tbUoEEDq9u89dZbamC2BAvSsiCtHVJ/euvB9evX1fuS+pDWH3l96S5m2brQsmVLFTToLR7SjUxajnRyP6XH5Tnlve3fvx/piS0WZAx+RbUlSruyohStAfjmcWWpiIiIyIr8+fPbVS9y4lysWLEE66R7kH7V3lo3KHkNGaeht5BIK4a0cCQOtJxFTvAt35cM5J41a1aClgfLMRShoaFqzEKhQoUwZYrW6yK5rlMy/kK6Pslzfvnll/jwww9VwCcmTJigtpk8ebK6Ly1D0tIg3cakFUNvObIk5ZRWCl1qj4sCBQqo95ieGFgQEXkAuRql/yDac7WNiIxBusfoXaF0b7/9NoYNG2buc6/TTw6le5Nl+lQZsJw41fSFCxeSbJsepJuVZZ9+/Wq+rmDBgqhSpYoqZ2rk/UnXIMsuR1mzZlUpWCVz0ssvv5wuKbXlu9NyrENKZCyDjGOoVasWxo0bl2p5ihQpgqeeesp8X1qQ9KDh/PnzCQIWOQak69KpU6cSpKu1JC1bEtDY+riQ95beqcgZWBAReQD5UU+cF52I3EeuXLmSrMuWLZtabNlWTrxlsWVbe8nJqHTRSYlcDd+xY4f5/unTp9XcDLpmzZqplLHSDUgPcuS+ZDqS8ROWlixZogIRSbtqOS5EskRJ1ikZgyBZmJxNTv4t0+rKuBZrmaSk9eXZZ59VrQpDhw5N9XnXrFmjnku6JwkZ+yApa/UB3dJFSbqKyVgLIRm4Dh06lGDsiQRaEnxKK4dkuJLtpaucbsuWLSk+LiSVrXTBSk8MLIiIiIgoWdJdSU5ax48fnyAbkiWZp0EGc0trgqQ3lbkjLLs5SepU6QrUo0cPNXhYAo9jx46p7j6JyXwPjz76aJLB5uXLl1cDq2UQd3oEFpKSVcqjj3NIjrQiSTco2UbGi+jq1KmjWlUkg5W8h6effloFXNICIQGKjMOQQE8CKmmN0utH5piQCQBlfEylSpVUUCD/J6l9dVIXvXv3VgGa1KO8Ttu2bRMEfyk9Lq0VkiY4vdPNMrAgIiIiomSVK1dOjTWQQEBOcOXktXr16gm2KVmypJrvYu3ateokV1obpDuOPoeFZISSE2p5XDIcydV+SYuqz6ZtOVmcPCYZo6yRyeUke5MMbJbWHBncbdkyYo0t2wgZfC3vT9K7JpcZSsZ5yPtv2rQpbNW8eXP88ccfqtVBTvAXLlyoMj5ZBkyS0UkGp0udyXuU1g3L7mUNGzZUQZ0Mdn/33XfNrR86eXzAgAHJPi4tILJfJHhKTwwsiIg8gPz46BlA5EcvvfvRElHmIifHsojkBlxLS4VMYiffR3JyLie3lt9F0h1IT3+aHAk0UprITk6MLU+O9axRKbFlGyFdyeTkXAaOJxdYyHiX1AaPBwQEJHkP0gUppW5IklI3uWBKJ91dU+rymtLjMuhd9o217nLOxHSzREQeIvEkS0REZB/pkiRBUeKMSu7s0qVLah4NGReS3thiQUREREQUnz1LZrmW7lrSvcsIateurVpBkiMtQyl19ZL3IgO5nTGQPzUMLIiIiIiI4lWrVs1QdVGnTh0VXCQn8XiKxNJztvLE2BWKiIiIiIgcxsCCiIiIiIgcxsCCiIiIiIgcxjEWREQewt/f39VFIKL0FhqsLcnxK6otRC7AwIKIyANIrviUBvcRkYfYNxfYPCX5x1uNBNqMysgSEZkxsCAiIiJyF/UHAJU6AzGRwJxO2rrnVwFZfLXbGdRaITNfy2RxMju0I2Qma5PJlO4Tt2VUGWNs2Hbnzp0ZmqkpI7nFGAvZAZ40UQkRERFRmkjgEFAbKGyREvVBGFC0hrY+gwKLNm3aYM2aNQ4/z8yZM/HGG2/AyOwp4+eff57izNzbt2/HsGHD4KkM32Kxf/9+vPTSS/j0008NM1EJEZHRxMbGYteuXep248aNVdcoIvJQx/8GVr7z8P78HkCeAKDTVKDqY+n60nI13svLK8XH7flfOcez9j/6ttHR0apVRL7TpDVASEtJXFxciq0l8p0oS7Zs2czrHjx4oO7rZZDnltvyfM4qo6XEzyX3IyIirJYltXp1F4ZtsZCD4eeff8bAgQPVDiMiopTJdyW/L4kyQVCxqB8QGpRwfUiQtl4eTwczZsxAw4YN1ViuadOmqRN7S3/88YdqxahRowYef/xxHDx40PzY9evX8cILL6j/lQsfr7zyCm7duqUe++qrrxJc4f/222/RqFEj1K9fH++99x6effZZ9dx6a8Bbb72FwYMHo2bNmmjQoIFqTbBmz5496nH9O/G///5T/7N8+XLzNs8884z5uS3L/+ijj2Lz5s3m7ewpowgNDcXw4cPV47LoZTx+/Lhq+bh7965af+HChRTrxh0ZNrBYunQpFi5cqHaen5+fq4tDRERElD7kanhUeOrL/ZD4lgprrQLx61aN0LZL6XlSaFWwZvHixZg3bx6+++471ZUnODgYt2/fNj++e/duTJ48GVOmTMGBAwcwdOhQfPHFFzh9+rR6fOrUqShUqJA62V+3bp0an/HNN98keZ0///wTc+fOVed+cmIvV/Hl+SytWrVKBQD//PMPxo4di88++wxnz55N8lz16tVTrQjHjh1T92V7aRHQA57w8HD1WIsWLZKU//XXX1fdlU6dOpWmMu7fvx9169ZVzyvPKWWU55IZvSVIkQx+R44cQdmyZW2uG3dh2K5Qbdu2VRGvoZuFLFO+xcXB9+5pIMgL0JvmmPKNiIiIUiIn+XMeAS7tdkI9mYCQq8CUVLqOl2ysDfi28RxLAos+ffqoK/7i3XffxYoVK8yPz5kzBz179lRX3aUlo3nz5ur2okWLMG7cOHVVvnDhwmrb3LlzY/bs2VZfZ8GCBerqv57hbvTo0Vi5cmWCbcqVK6fKIqRlQU7MJUAoX758gu2km5G0sMhJvjyf/G3atKk5CNi3bx/KlCmDYsWKYfz48QnKL8FGy5YtVfnHjBljdxkDAwPx9NNPq9vt27dHkSJFVJBVqVKlJO/Z1rpxF4YNLPLkyWP3/+j96TKK197v4b3lI3VbetVVlRtbHz4e1/IdmCTtG9kuNlbVpXYzVt2ntNM/Dxn5ufBURq9LKZfeNcGoZXSXunQnrEsPqEuTSXUfycjLqCaYECfv08bAQloE+vbta64bOQEuWrSo+s6RdfL4tm3b8NNPPz18DZMJvr6+6nHp3iNdiaT7kJzcd+jQAd26dVMtCrKdLLKdnHxLF3j9dXLkyIFSpUqZX0e2CwgISLCPZBvp7mRtv8lrydiz5557TgUW0hIhXamktULWSwCUXPlFs2bNzK9rTxmLFCmSoDw5c+ZEZGSk1e/plOrGKN+X9ry2YQOLtNCb3DJKluwNkLXFTHjHPkDlHa+rdSebfoE4n+zqdnT2Aoix6GNIqfOOiUSd+NtyBSJOT59HDpEmV/LsupQfK+meIA4dOuRwCsjMXJfuiHXp5nVZaxK8q99PdbPctw6j4p7U56g403AywgporQvWxPnkkC8Ku75fzp8/n2DchAykljECsu7+/fvo16+fujqfmDwuqVela5Tcli5J0oqxbNkyDBkyBNeuXUNISIh6THqpyEl+gQIFzP8vg50lM6g8LttKUGBZDuk6JOMnLNfp5Hn27t2LrVu34ubNm+q+nOQvWbIEmzZtwlNPPWVT+e0tY1hYWILyyPNfvHhRrTt37pyqO/3xlOrGHT/jHhVYSNOTHDAZTvorxgcW5Zo+Dh9f+1tbyKIu41sUpS8i69LxqwzyZSSD0ZglyLPrUsonAwZFrVq1DFlGd6lLd8K6zGR1GdcQphNfqYHbXlbGWZik3SNPAMp1GAh4O+89VK1aVZ0s691/5LtGxlhIVyJZV7lyZVy9etX8uNSlnCjL77hc0dczIMngZCFdh6QLkWwv4xTu3Lljfh45CdefR4KIGzduqKygsm7Dhg3qBN9yMlB5XmkxsDZBqD7QXIIL6RYl4x6kleLEiRMqAOjVq5f6/8TlTzwfhaNlzJEjh/nxe/fuqSxU+uMp1Y1RjksJnGy9eO9RgYVUuEsq3eI1XVYGT8G6TKdq5XGZGeoyb968hi+jJXcppztgXWaSupRydZ6qZX9SnacsgwsvrTtVpynwyfowxaoz9O/fH2+//baa1E2CjI8//lidEOspVgcNGoQBAwaoMQmPPPKIOnGXrEiSvUnWS/YlOamXtK1i48aN5gsg0gIgi9yW15ExDZLNqWLFimrQs5zU6q9jua35XXt5WU31qpNA4scff1TlkW2ku5G8RuvWrVVXLZG4/DLQeuTIkWqddKNytIxCf1y6kUm3KAlspNUjpboxynFpz+t6VGBBRJRZyRe/ZEEhIg8n81T0mqdlh7JMOavmsZiSLvNYSBchSasqmZPkinv37t1RvXp18wmnjA+QYEMyJclfGYwsJ+gyyFnIyfekSZPw2GOPqSvwsv0nn3yiHpMWAX2W6k6dOuHKlSvqtUTv3r1RvHhx8+NypT/xjNZytT+lrp8yCFsyOUl6VyHBkTyHBBY6a+WXsksQ4awy+sTXlQyAl+9qGXg+a9asFOvGHXmZUprJxCCkgmUgizQ/WSORokTHVapUcV1XqEkB6mbsiEvsvsO6NAy9OVqaVA17Bc5NsC5Zl0bE4zIT16WklNWzP/VdDJRv69TuT66oy8QTzUl3JDnRnj59ugoIjCCjyxhrgOPSnvNst2ixkOYrIiIiokxPT3UfE/mwKrLnBoKPuH2qe0ljK4OsP/jgAzUuQebNkCyh0lXIKNyhjK5k/LQhRERk01UtSZ8oC9O4EnmwfXOBWa2AOZ0erpPbsk4WedxNyTgOmRRZ5pTo3Lmzyr4kc2Rkz65l2zQCdyijK7lFiwUREaVOMpUQkYerPwCo1Dn5x920tULIDNSSetXI3KGMrsTAgoiIiMhduHFXJ/J87ApFREREREQOY2BBREREREQOY2BBREREREQOY2BBREREREQO4+BtIiIP4ZIJQomIiOIxsCAi8gAyI2vDhg1dXQwiIsrE2BWKiIiIiIgcxsCCiIiIiIgcxq5QREQeIDY2Fvv371e369Wrp7pGERERZSQGFkSeJjRYW0RcHHzvngaCvADv+AZKztrqsSIiIlxdBCIiysQYWBB5mn1zgc1T1E25Zl1Vbmy1eLzVSKDNKFeVjoiIiDwUAwsiT1N/AFCpMxATCczppFbF9l8Bn+y5HrZYEBERETkZAwsiT6N3dYoKf7iuaA3AN48rS0VEREQejlmhiIiIiIjIYQwsiIiIiIjIYewKRUTkIXLkyOHqIhARUSbGwIKIyAPIvBWNGzd2dTGIiCgTY1coIiIiIiJyGAMLIiIiIiJyGLtCERF5gNjYWBw8eFDdrl27tuoaRURElJEYWBAReYjQ0FBXF4GIiDIxdoUiIiIiIiKHMbAgIiIiIiKHMbAgIiIiIiKHMbAgIiIiIiLXDt6+dOkSrl27Bi8vLxQuXBglS5aEs8XExKjnZ4aTTCIu9uHtizuBiu0Bb2a3ISIiIvK4wCI4OBg//vgjli1bhuvXryd4rEiRIujYsSP69+/vcJARFhaGUaNGYdOmTep+9+7dMXbsWGTNmtWh5yUDO/43sPId812fBb2APAFAp6lA1cdcWjTK5CTgvbAN+a7sBvzDgLLNDRnw8vuRiIjcIrCIjo7GrFmzsGDBArRo0QLjx49HtWrVULBgQcTFxeH27ds4evQotm3bhp49e+Lxxx/HG2+8gRw5cqSpYBMmTFCpE3fs2IGIiAgMGjQIM2bMwLBhw9L0fOQGQcWifgBMCdeHBGnre81jcEGuOzZXjYBPyFWUk/v/wJABr7TqNmvWzNXFICKiTMzmMRa///47vL29sWbNGkyePBnt2rVD0aJFkSVLFmTLlk3dbt++vQo4Nm7ciAIFCmD+/Plpbq1Yvnw53nzzTfj5+amWkKFDh2LRokVpej5yg6vBq0YkDSqU+HWrRibsJkWUkQFvyFXrAa88TkRERPa1WDz11FO2bgpfX18MHjwYaXXy5EnVClK5cmXzOrl969Yt1RVLghjyIP/tSHriloAJCLkC7P0OqNsPyOqbgYWjTCvVgNdLC3grdzVktygiIiK3Gbz94MEDHD58GHfv3lWtEzVq1HBa/96QkBAVnEhLiC5Pnjzqr7xecoFFZGQkXCIqAj7QyhoVEQEfEyc0t1lsNLIe+g02HTkr34Fp9WjEFaqKuIB6iAuog7hidWHKVw7w8krz7vNYPC7TLvIOshxZgGw2BLz3T29EXKmmcLXY2FicOHFC3a5SpYqhE15IWaV7rXRzNXI53QHrknVpRDwuPasu7Tm/9jKZTNYux6VoyZIl+PDDD9UYCOmqJH/z5cunBld37twZjlq/fj2GDx+OAwcOmNfdu3cPDRs2VIPGK1asmGB7qWz5Qd2yZYsKSsj4spmiUBdH0Bj/IC9CbfqfSGSHLx4kWR+BHLiComq5jGLq730vtmqQbXKY7qMYriMAwSiGawjAdeTDPZur73d0wVGvh62rREREnkQu7rds2VJdtMqZM2eK29p9af3MmTMYM2aMWiRTk7QqREVF4e+//8bo0aNRvnx5BAYGOlJ+1QIi0ZG0imTPnt0cWAgJYJLTr18/1dKR4aLC4fN5Ve3ma0fg46u1rpAV4TeQdf93yHLgJ3g90PapKWdBIOYBEBUGLyvdTkyy1q8YTIN3IzIsCN5X/4F3kCwH4B18GDlj76MiLqhFF+df3tyiERtQF6ZCVQGfTJZRjMdlUg9C4B18BN7Bh7Tl2mF433143FiKy1UU3uHBqVZz16deQGeDtFjs2rVL3W7cuLGhWwKkrMeOHVMJQIxcTnfAumRdGhGPS8+qSzknv3DB+m+lw4GF/HB17doVvXr1Mq+T4KJHjx6q1WDDhg0OBxby//KcR44cQf369dW6Q4cOoVixYioLVXIkqEgtkkoXWeRkOEq7mTMnfHxdUAaju/kvsPMr4OACIDa+1aFABaDpa/Cq+RRwZk18Vijp0mQZXHipNV6dpyKnXx5AlmKVAPTRHo6JAq4dBa7sBy7vBS7vA26fhfcdbcGxxdp2WXIAxWoBJRoAxesBJeoDeUt6dheqzH5c3g8Bgg8DVw8AVw9qf2+ftb5tvjJAQB1tKVZbHSve2f2Az6trA7WtjrPwUtmhcgS2McQYC/nx0bujyvegkU/Y9bIavZzugHXJujQiHpeZty7tDizkxP7KlSuQHlQycZ0lGf+QuJtSWkjlSWvI+++/j48++khFSp999hmee+45h5+bMpic6G//HDix7OHJmZzcN3sdqNTl4QmZpO2UlLIyj0WonMjFU2k9pySf1jNLNqB4XW1pOEhbF3E7PtDYB1zZp/29fxe4tFtbdLmLAMXrAyUk0GignVTKySS5nwdhSYOIW/9aDwj8Sz0MINTfWkDO/NafV1LKWg14hUk7Ng0QVBAREbllYNGgQQPMnj0bL7/8ssoUJQOpJaBYvXo1du7ciS5duqixDkImyStbtmyaCvbuu+9i6tSpePHFF1V3KGkhYWDhJuLigH/XAtu/AP7b/nB9YCeg2TCgVGPrLQUSPJRrDUzRJleM7bMIPmmZeVtOEit20BYhw4hunY0PMuJbNaSVI+wacGq5tggvb6BQFS3QUAFHA6BQJZ44Gk1UOBB8JGEQcfO09SBCWqUCaj8MImRJLoiwRg94JTtU4oHchSoDVR51/P0QERFl1sBi6dKlOH/+vFp277a4+htPBl3rBgwYoOafSAsJJmQwuCzkJqRb0tHFwPYvgRtadhp4ZwVq9lJdnlC4SurPYRlElGrinJN6CWIKVtCWWvFpk6MjgaBDWpAhwYa0cNy7BFw/pi3/zNO2y5ZbOxmVIEO6T0nA4VfE8TKRbaIitCAwQRBxCjDFJd02T/GELRESUORKvuukXcFF5a6IPb8N/x3bjdJlysHnryHAjZPAqZVA5S7cm0RERGkJLCRYkIXI7EEosP9HYNfX2nwTIpsfUP85oNHLQN7ixqssmQtDWk5k0YUGJ+w+deUfNaAcF7Zqiy5vqYfdpyTQKFaTc2s4Q/T9pEGEnLybrEyM6FcsYSuEBBG5CyPdSIBbpjnu3M2N0tVqa8HntmnA2rFay5hBEgPIJKZERESuYsh5LMhNhF4Dds8E9n4PxGd4UuMWGr8M1BsA+PrDrfgVBap00xZ9gjQ5sdW7T0mrxvUTwL2L2nLsT2077yxA0Rrx3afiu1Dl59waKZIsYAmCiINaK1dcTNJtcxW2CCDigwjZV67U/A2tVevWGeCfH4EGA11bHkAN6pN0gERERG4VWKT3PBZkcDfPADu+Ag5Jhict6xAKVASaDQVq9gayaCmC3Z5cpS5STVvqPfcw05CcDOutGrKEX48/QT4A7J2tbeebLz77VHyrhgwut6dvv6d1kZMr/HorhCwSoMVFJ91WUg9bBhDyV1onjJa9K0ceoPVIYMVwYONkoEYvbR0REVEmZsh5LMigLu3VMjydXG6R4akh0HwYENhZ+mHA48nJY7lW2qIPDJexGapVIz7lrYzdiLwD/LtOW3SSXtfcqlEfKFLdMF1onCY2Grh+PFEQcfxhAGrJN3/SIELGSRgtiEiOBJvSYifZpyRRQbsxri4RERGRSxlyHgsyWIYnmWNCTpwu7ni4XlLFSspYyzEKmZGcBEv6UlmqP5lwbg3L8Royf4KcgMpy+FeLuTVqxw8Kj2/dyFvCfU6sJYiQrmJ6EBF0EAg++nCeEks5/JMGEe4+j4gEhe0nAAv7AjtnAPWfd+l4ori4OBw9elTdrl69OsdbEBFRhjPkPBZkAHJyfOQ3YIdkeDr5MMNTrd5A06FaGlZKfW4NDE46t4aehUrNrbFLW5LMrRG/GGVujdgYLRtTgiDiCBBzP+m22fM+DB70v/6l3TuISE7lrlr2sos7gY0fAt2/dllR5Dv59u3b5ttEREQZzbDzWJCLyBiC/T8Au74BQuPz9mfPA9QfADR6SZuwjuyXeG4NaQmSVgxzq8Ze4NqxlOfW0Mdr2Dq3hgw+18mJr61zgsj/ybwQlkFE0GEgJjLptnJsyARz5kCiDpCvrGcGEdbI++z4AfBdO+DgL1riAhnIT0RElAkZdh4LymCSalWCiX1zgAch2rrcRYEmQ7S+5Dnycpc4k4xHKVhRW2r3STS3hkUWKqtza/gBxes8nMRPWjYSp1o9/rc2i3k8nwW94mcxn5pwFnMJcKR7lj4eQg8iosOTllnm9FApXhMFEZlhbE1KpP6r/Q849gewZgzQb4mrS0REROQSnMcis7txWuvudHjhwwG2BQO18RM1enpOhid3YPPcGqHA+S3akmBujfjuUzIfxIaJSWeiDgkCFj2rzS0iLSEqiDikzdWRpCy5ErZESEAhg88zexCRnPbjgJPLgHMbtQH7Fdq7ukRERETGDSzu3buHvHntu2otXaT8/d1sLoPM4uJubUC23uVGlGysZXiq+AhPII08t4akarVMdytjYMxza/yRwpPFBxq7v0m4OmtOoGjNhEGEtKQ4Y9bzzCJfGaDhYGDndK3Volwb1h8REWU6NgcWMm5C5q945ZVXULeuDEpN3oEDB/DNN9+gdevWePrpp51RTnIG6fZyepUWUFgOGK7cTRuQXaoR69no5GS/aHVtSTy3hnShkgxel5J2UUxC9rkMPFZBRCDgk+a5MknX4i3gwE9ael0Zb1H3WdYNERFlKjafTTz66KNqoLZMgiezbrdv3x5Vq1ZVWaIkzeGtW7dw7NgxbN26Vd0fOXKkCizIILMcS4an7V9qmX2ETzag1lNAk9eAQkwP7DFza8iVc1sCi2pPADV6ZETpMtcA/ZbvAGve1TJEVf8fkC2Xq0tFRESUYbLYmxFKBm9v2rQJy5Ytw8qVK3Hjxg2VdlYCjHr16mHYsGEq6PBmX2zXu3/PIsNTkEWGp+fjMzwVc3UJydkkXa0ztyP7NBwE7JkF3P1Pm9ui1cMB9OnNx8eHF3OIiMil7O7/IEFEmzZt1GKZLz3xnBbkQjJIV/rR75v7MMOTXzGgsZ7hKQ93j6cq3VTL/iTHQOLB24qX9rhsR84nyQ5kIPfi54FtnwN1+wN+DOKIiChzcDjFiwQUDCoM4sYp4K9XgM9raOMoJKgoVBl4/Gvg9cNAs6EMKjLDGAxJKaskDvbj73eawoHF6UlSz8pM6pKyd9PkdH0pIiIiI2HuSE9wcRewoA8woyFw4GcgLhoo1RTosxB4eSdQp682GzRlDjJPRa95WkYpS9JSIest57Gg9Js0T8jcIxLwZwAZ2ybj3GSR20RERBmNqWDcOsPTyvgMT/pgXS8t04/MQVGyoYsLSC4lwUO51sCUkupubJ9F8LF15m1ynHQ1k8xbMrfF2nHA07+me61Kt1QZ8yYqV66c7q9HRESUGAMLd8zwJJPZSYanW2csMjz1AZq+ps0/QCQsg4hSTRhUZLT244FTK7ULAOe3AmVb8LgkIiKPlubAIjw8HGfOnEGuXLlQsWJF3L59G/nz53du6Shhhqd9c4BdM4GwYG1d9rxAgxeARi8m7fZCRK4lQb5kYNs7G1jzHjBoIyeeJCIij5amMRaSZrZt27bo3bs3/vrrL7Vu8eLFGDJkiDlLFDlJyFXtpGRaNWDdeC2oyFMc6Pgh8OYxLQMNgwoiY2o9EsjmBwQdBI4udnVpiIiIjBVYXL58WU2SN3nyZDVnha5nz544deqUmiCPnOD6SWDJEODzmsCOr4CoUKBQFaD7TGDoQaDpq0B2P1Y1kZHlKgi0eEO7vf59IPq+q0tERERknMBi165daNeunWqxkAmZdPny5VPrJSMJpZG09vy3A/ilN/B1I+DgfC3DU+nmwNO/AUN2ArX7MMMTkTuR+WOklfHeJWD3TFeXhoiIyDhjLKKiohATE2P1sXv37qFQoULOKFfmy/B0aoWW4enynviVXkCVbkCzYUCJ+i4uIBGlWVZfoO17wJKXga3TgLr9gJwcj0ZERJ7H7sCiVatW+Pjjj7Fs2TIVZIiQkBD8+uuvat2SJUvSo5yeSbpFSIanHZLh6V9tnU92rVWiiWR4quDqEhKRM9TsDez6Ggg+Amz+COg8xen16u3tjRYtWphvExERGT6wKF68OKZNm4YJEyYgKCgI2bJlw+zZs+Hv749JkyapDFGUisi7WoYn6RYRdk1bl0MyPA0EGkqGpyKsQiJPS/3bYSLwU3ctS1TDQUCB8k59CS8vrwTdU4mIiNwi3WybNm3QrFkznDx5UgUXBQoUUBMy5c6d2/kl9CT3rmhXLff/AESFaeuk73WTV7TuERyMTeS5yrcBKnQA/l0LrJ+gzYJORESU2QOL1atX486dO3jqqadQs2ZNtW7+/PmIiIjAoEGDkOnExT68fXEnkHiG42vHtcxORxYBcfHjUwpX1WbIrv4k4JM148tMRBmvw/vA2fXA8b+Ai7uBUo2c9tRxcXE4ffq0uh0YGMjuUERElOHs7oh74cIFjB49GuXLJ2zGb9KkCebNm4fDhw8jUzn+NzCjofmuz4JewOfVtROHC9uB+b2Ab5oAh37RgooyLYC+i4GXdwC1nmJQQZSZFKkK1O6r3Zb5aZw474/MIRQcHKwWzidERERu0WKxc+dO1RWqQYMGCdaXK1cO7du3V+lo9VaMTBFULOonP+lJJ7VT63VeQNXHgKavAyXqZXQpichI2rwLHP1dywB34m+g6uOuLhEREZFrWixkgKA0uVt9Mm/vZB9zxJdffomDBw/CcN2fVo1IGlQkVu854LX9Wn9qBhVElKcY0PQ1rR7WjgNitOx6REREmS6waNmyJdavX4+ff/4ZYWHaAOTIyEiVZvb3339H69atnVrAmTNnYsaMGbh58yYMRSayk5aJ1FTv4fTsL0Tk5poOBXIVBu6c1zLEERERZcauUAEBAWoeizFjxmDixInIkSMH7t+/j/z586sUtJIdyhkk29T48eNx6tQp9RqGo6eJddZ2RJR5ZM8NtBkNLBsGbJ6ijbfy9Xd1qYiIiDI+K1THjh3VOIujR4+q7FAy23aFChXg6+sLZ5GuT2XLllVBTIcOHWA4uYs4dzsiylzqPAvs+ga4eQrYNk3LGEVERJTZAguRNWtW1KlTB+mlc+fOarFHbGysWjJEiUbw9gsAQoPgZWWchUkGbOcJQFyJRlKwjCmTJ4iNhZ6oV+1L1h3r0iD07xbnfcd4Ae0nwOfXp2DaNRNxdZ8H/Es6VD59jFuGfQ8api4zL9Yl69KIeFx6Vl3a89ppCixkpu2FCxfi9u3bSdIaDhgwAEOHDk3x/2/duqVS1lrz+OOPo0uXLmkpljmHe0bxrzQY5faNV2GFl8V6rUZMOBc4CHcPH8nQMrk775hI6OHqsWPHEJfFea1gmQ3rMn0cOeLEz7SpECoWqI08tw7i7h9v4ULd0Wl/KpMJuXLlUrcl7bck2shUdZnJsS5Zl0bE4zLz1aXdgcX+/fsxffp0vPbaa6r7k2SCslSyZOpX3OTHTybXs0bS1qaVTAqVM2dOZJjatRFXpiy8V49ULRdmeYojruMklKnyaMaVxVNEhQMrtZvVqlWDj28eV5fIfbEunX7FRr7Ya9SoAR8fiwkwHVX0M+C7NihwZR38O48GitWGp0u3usyEWJesSyPicelZdSkTYNt68d7uwEKuIkuLwsCBA5FWMhhbxmg4m1R4hld69e5AhbbAFC2giu2zCD4V28PHcuZtsp3F/nPJ/vQkrMt0qlYnH5cl6gI1ewOHF8Jn3Tig/1LJ643MgJ9x1qUR8bhkXRqRjwvPiex5XbvTzRYrVsx4qV9dzTKIKNUk4X0iotS0HQP4ZAcubAVOr05Tfcn4CrmiJEt6zCdERETk9BaLunXrYtq0aRg5ciSaNWuGvHnzJukKJdmciIjIRjJou/HLwPbPgbVjgArtAZ8sdo+xuHpVm1unfHnOnUNERG4QWPz9998IDg5Wy+rVSa+s2TJ4216ffvqp0+bHICIypBZvAv/MA26eBg7MA+o/7+oSERERpW9gIYGDLBmpefPmGfp6REQZLkdeoPVIYOU7wMbJQI2eQHY/7ggiInIbdo+x0IWHh6tJ7M6cOaPuS+pZIiJyQL0BQP5yQPh1YPuXrEoiIvL8wGLlypVo27Ytevfujb/++kutW7x4MYYMGZJkXgsiIrJRlmxq0jxlx1dAiDZmgoiIyCMDi8uXL2Ps2LGYPHkyhg0bZl7fs2dPnDp1Clu3bnV2GYmIMg+Z/6ZkYyAmEtj4oatLQ0RElH6Bxa5du9CuXTvVYmGZ1zZfvnxqvcxzQUREaSRzWHScqN0+MB8IPsqqJCIizwwsoqKiEBMTY/Wxe/fuIUsWu8eDExGRpZINgardJYkssHasTXXj7e2Nxo0bq0VuExERZTS7f31atWqF9evXY9myZSrIECEhIZg1a5Za17p16/QoJxFR5tJ+HOCdFTi7Hji7IdXNvby8kCNHDrXIbSIiooxmd/NC8eLF1QR5EyZMQFBQELJly4bZs2fD398fkyZNQsWKFdOnpERkm9BgbZE++rrgI0D2XNptv6LaQsYm2aEaDgJ2fQ2sGQO82Arwftj9lIiIyGjS1G+pTZs2atbtkydPquCiQIECagK73LlzO7+ERGSffXOBzVMSrPL5scvDO61GAm1GsVbdQcu3tXEW144Ch34F6vRNdtO4uDicP39e3S5btiy7QxERkXEDi6tXr+LSpUtJ1ktLRWxsrHnQdkBAAEqWLOncUhKR7eoPACp1Vjdj4+JUtrZKlSrBR+93z9YK95EzP9ByOLB2DLDhA6DaE0C2nFY3lVTf+nd0mTJlMrigREREdgQWy5cvx2effZbqdoMGDcIbb7zBuiVyFcuuTrGxiLxmAorVAiyyuJEbaTgY2DMbuHcR2DVDa8UgIiJy58BCAgZZiIgoA2XNAbQbC/wxENj2BVD3OSB3Ie4CIiIyHKfmJIyMjMSVK1ec+ZRERFT9SSCgDhAVmmT8DBERkdsO3pbxFJIRasOGDWo+C+nXq68PDw/HwIED8dZbb6VHWcmTMZMRUfJkfEzHD4AfumqD8xu+CBQKZI0REZF7Bxbbtm3DX3/9pcZRXL58WQ0M7dmzpxqDIRmiXnvttfQpKXk2ZjIiSlmZ5kClLsCpFcC68UCfX1hjRETk3oHFuXPn0KVLFzz33HMq3ezQoUPx2GOPoVu3bujTpw8OHjyIhg0bpk9pyXMxkxFR6tpPAE6vBk4tBy5sB8o0Y60REZH7BhYyV0WuXNpEW6VLl1bpDcPCwtR6mdvi8OHDDCzIfsxkRJQ66f5Urz+wbw6w5j1g4Hqtm5TqLeWNBg0amG8TERFlNLt/fWRm7S1btuD69evw9fVFuXLlsGTJEkRFReGff/5BlixpmnOPiIhs0XoUkC03cPUf4Ngf5tVeXl7qoo8scpuIiMjwgUXt2rXRqFEjzJ8/X91/9dVXMWnSJNSpUwcnTpxA587axFxERJQOchcGmg3Tbq+fAMQ8YDUTEZEhpKl5YeLEiaqFQkggERgYiLNnz6JevXooUKCAs8tIRESWmrwC7PseuHsR2DMLaPoa4uLicPHiRfVwqVKl2B2KiIiM32IhLRXvvPMOdu7ciejoaLWufPny6NixI4MKIqKMkC0n0PY97faWj4GI2yr194ULF9SipwEnIiIydGBRv359+Pj44O2330bz5s0xZswYFWTI1TIiIsogtfoAhasB9+8BWz5htRMRkfsFFpUqVcLkyZOxfft2fPTRR2qSvGHDhqFly5b44IMPVJcoIiJKZ94+QMeJ2m3pDnX7PKuciIhcKs05CbNmzYpWrVqpIOPbb79V3aF++uknNXkeERFlgArtgPJtgbhoeG2MDzKIiIjcafC2dHvav38/1qxZg7Vr1+LevXto164dZs6cqbpHERFRBukwETi7Ed7HlyBP9qYIyVuJVU9ERO4RWEgwMWHCBISGhqruTyNGjECbNm2QI0eO9CkhERElr2h1oHZf4ODPKH92Lg7UmczaIiIi9wgssmfPjuHDh6NDhw5qtm0iInKxtu/CdPR35A05gYI3dwFo6eoSERFRJmT3GAsZV/HEE0+omV0PHjyIM2fOqPW3b99Oj/IREVFq8gRoc1sAqHJlIbxNsawzIiJyj8HbK1euRNu2bdG7d2/zYO3FixdjyJAhzJ9OROQCXs2HAbkKwefueXjt/4H7gIiIjB9YXL58GWPHjlXZoCTNrK5nz544deoUtm7d6rTCyaDwHTt2qHkyQkJCnPa8REQeJ7sf0HqUdnvzFG1+CyIiIiMHFrt27VIZoKTFQibK0+XLl0+tP3bsmFMKtmHDBjWOQzJNffnll+q2zJ1BRETWs/VdLNQG0XnLAhG3gG2fsZqIiMjYgUVUVJSaFC+5FoYsWdKUwTaB6OhojBo1Si3z5s3DggUL0L9/f5WBioiIkjKZTDh34SJOlnxaW7HrG+DuJVYVEREZe/D2+vXrsWzZMhVkCOmmNGvWLLWudevWDhfq5s2baNq0KR555BHzOklpe+PGDdy9e9fh5yci8lS3CjSAqXQzIOY+sOEDVxeHiIgyEbubF4oXL45p06apuSyCgoKQLVs2zJ49G/7+/pg0aRIqVqzocKGKFSuGzz5L2Iy/e/duFClSRL0OERElw8sLce3fh8/37YDDC4HGLwMBtVldRESU7tLUb0laD5o1a4aTJ0+q4KJAgQKoXLmyzfNaREREYOPGjVYfq1SpEipUqJBg3b///ovp06fjvffeS/F5Y2Nj1ZLhYmOhjzZRr++KMngQfR+6ZF96GNZl5qlLKZeMs1C3i9SEV/Ue8D66GKY1YxD3zJ8q4DAKo9elO2Fdsi6NiMelZ9WlPa/tZZKOuXZYvXq1mr/CkfEOMufFxIkTrT7WpUsXNVBbd/bsWTz33HN49tlnMXjw4GQDlRMnTsBVvGMiUWdlV3X7QOfliMvi67KyEFHmJEGFPq+QtBznuH8d1Tb2h3dcNM40nISQIo1dXUQiInJjVapUQc6cOZ3bYnHnzh11Iu+I/PnzJ+nqZM2hQ4fw8ssv44033lDpbFMTGBiY6htOF1HhwErtZrVq1eDjmyfjy+BBJDI+cuQIatSokSDzGLEuXcnox6WULzQ0VN2uVauWVsaIl4CdX6HCuR8R12Eg4O14co3MUJfuhHXJujQiHpeeVZdy3n/69GmbtrX7V0YGVc+ZMwdTp05F1apVVTeoxGMwSpcuDUfJ3BUyT4a8jq0DwqXCXVLpFq/psjJ4INYl69KIjHxcent7Jyxjy+HAwZ/hdfMUfA4vAOo9ByMxcl26G9Yl69KIeFx6Rl3a87p2BxZr165V4yp+/vlnq4+/8MILCSbOSwuZhE9m8ZYMVOHh4Vi+fLn5MQkycuXK5dDzExF5GgkqateunSDAgK8/0GoEsGoksHESUL0HkN22sXBERET2sjuwkMBBlvR04cIFcyvFunXrEjzWqFEjBhZERIl4eXlZz5pX/wVg97fAnfPAjq+ANvGzcxMRETlZmjvcPnjwAIcPH1bzSkh3KOn7lTVrVqcUqnnz5mohIiIHZckGtB8H/PYcsONLoP4AwK8oq5WIiIwRWCxZsgQffvihGijo5+en/ubLlw9jx45F586dnV9KIiJKNSuUdFPV5wIyd4cSVbsDJRoAl/dqXaIe+5K1SURErp95W9IZjhkzBm+//bZqsdi7d6/6+9Zbb2H06NE2jxonIiLnkczh8v0sS5Is4jKHRccPtdsHfgKuuy49NxEReS67A4tdu3aha9eu6NWrl5p1W8jfHj164H//+x82bNiQHuUkIiJHlGoEVHkMMMUBa8eyLomIyPWBRcGCBXHlypWkV8QANd7C6uBBIiJyvfbjtbkszqwBzm1ydWmIiCizBxYNGjRQKWBl4rpNmzbh5MmTqhVjwoQJau6JQoUKYcuWLWo5f/58+pSaiIjsV6C8liVKrHlPBmawFomIyHWDt5cuXaoCBll2796d5PHhw4ebbw8YMABDhw51vJREROQcMq/FoQVA8BHg8EKgdh/WLBERuSawkGBBFiIickO5CgAt3gTWjQc2fABU6w5k9XV1qYiIKDN2hSIiIjfX6CUgb0kg5DKw6xtXl4aIiDwEAwsiIg8g81bIRKWyJJjDwhppoWg7Rru9dRoQfjNDykhERJ6NgQURkQfw8vJCgQIF1CK3U1WjJ1CsFhAVCmyemhFFJCIiD8fAgogoM5JWjQ4Ttdv75gA3/3V1iYiIyM0xsCAi8gBxcXEIDg5Wi9y2SblWQMVHgLgYYN249C4iERF5OLuzQn3//ff4/PPPrT7m4+ODnDlzolKlSnjjjTdQs2ZNZ5SRiIhSIZOWyrxCQuYTslmH94F/1wInlwH/7QRKN2FdExFRxrRYPPHEEyhTpgxatmyJL7/8EgsWLMDXX3+Nrl27qr69MlFetWrVMHDgQFy7di1tpSIiooxRuDJQt9/DSfNMJtY8ERFlTIvFoUOH4O/vjxkzZiRY36JFCzUZXnR0tJok79atW1izZg2effbZtJWMiIgyRuvRwOHfgCv7gGN/AtX/x5onIqL0b7G4cuUKSpYsafWxgIAAnDt3znz77t279peIiIgyll8RoNnr2m2ZOC/mAfcAERGlf2DRoEEDrFixQrVGSJ9e3ebNm7Fo0SLUr18fERER2L59e7IBCBERGUzTV4HcRYG7/wF7v3N1aYiIKDN0hZKB2aNHj8aIESPUkj9/fty5c0cFGa+++ioaN26MWbNmISwsDB07dkyfUhMRkXNlywW0fRf4+zVg80dA7acB33ysZSMKDdYWERcH37ungSAvLYWw8CuqLURERg8sRK9evdC5c2fs2bNHjaWQDCR169ZF3rx51eN9+vTBCy+8oLJEERGRm6jdF9j1DXD9OLDlE+CRD11dIrJm31xg8xR1U35lq8qNrRaPtxoJtBnFuiMi9wgshJ+fH9q1a5fsY0RElHG8vb1RtWpV8+20PYmPNmne/CeBPbOAhoOAfGWcU0BeZXee+gOASp2BmEhgTie1Krb/Cvhkz6U9ztYKInKnwGL27NlYuHAhbt++nWCchRgwYIDKDkVERBnHy8sLhQsXdvyJKrQDyrUGzm0C1r8P9JjjjOLxKrsz6V2dosIfritaA/DN49SXISJK98Bi//79mD59Ol577TVUqFAhyZUxDtgmInJjXl5aq8W3LYGjvwONXwFK1HP8eXmVnYjI49kdWBw7dgxdunRRE+AREZExSOvxjRs31G0Z9yYtGGlWrCZQqw9w6Bdt0rwBK7SAwxG8yk5E5PHs7ohbrFgx3Lx5M31KQ0REaRIXF4fjx4+rRW47rO17QJYcwMUdwKkV3CtEROT8FgvJ/jRt2jSMHDkSzZo1M2eCsuwKVbZsWXufloiIjCRvcaDJK8DWT4G144CKHQGfrK4uFREReVJg8ffffyM4OFgtq1evTvI4B28TEXmIZsOA/T8Ct84A+3/QskQRERE5K7CQwEEWskifKCn/dMFHAMuUf0z7R0TuKkceoPVIYMVwYNMUoGZvbR0REZEjgcWlS5cQExODLFmy4Pz588lul6m6QllMUqTz+bHLwzucpIiI3F2954DdM4Fb/wLbPwfajXV1iYiIyN0Di/Xr1yMsLAy5c+dW6WaT89xzz+HVV19FpqCnT5TJieLicOrUKVSqVAk+egpetlYQkbuTcRXtJwAL+wI7ZwD1X9DGXxAREaU1sJCAwdrtxBJPmJdWoaGh+OCDD7BhwwZkzZoVjz/+ON566y3VYmIYll2dYmMRec0EFKsF+Pi4umRERM5TuStQqqmWIWrjh0D3r1m7RETkeLrZ7du34969e0nWSzepOXPmYObMmXCG8ePHq9dZs2YNfvrpJ/V31qxZTnluIiJPI/NWVK5cWS0OzWFh/cmBjh9otw/+oo0lIyIicjSwOH36NJ599lnzRExi3759eOKJJ/DVV1+hfPnyTmmtWLVqFd5++23ky5dPPWebNm1w4MCBFP8vMjIyQYtJVFQUwsPD8eDBgwTbyTpZLHO9R0dHq3X3799P87ZyX9bHxsYmCLhknZTNUkREhM3byn1ZL4/r5P/s3VZe01p55f2kZVupE71+LEl9yzqp/7RsK/tQfx/W9mfibfXnTeu+d8ZxYm1/2rNtavvekeNE1ifen44eJ8ntT0ePE8v9ac+29ux7R74jpN4c+Y6wZ9/be5zIUrBgQRQtWhTe3t7O/46Q2berPyk1j9iVoxDhyL6PMSE8yuTQd0RK+zNTfUdEa3Xp6G8JvyO0/enq84j0/I7I6POI5M6RMuo8wpO+Ix4kel5XnEfYzGSnBw8emIYMGWLq0KGD6dChQ6aRI0eaKleubHrrrbdMwcHBJmeLjY01HTx40NSiRQvTokWLrG4THh5u2rdvn6lSpUqm69evm9d/8MEHcoSYBg4cmGD7nDlzqvXnz583r/vss8/UuqeffjrBtgULFlTrjx49al43a9Yste7xxx83r4uJiTEVK1ZMrd+zZ495/c8//6zWtW/fPsHzVq1aVa3fuHGjed2ff/6p1jVt2jTBtvXr11frly1bZl63Zs0ata5WrVoJtm3VqpVab1lX27ZtU+sqVKiQYNsuXbqo9XPnzjWvO3DggFoXEBCQYNsePXqo9dOnTzevO336tFqXN2/eBNv2799frf/oo4/M6y5fvqzWZcmSJcG2cizJ+nHjxpnX3bx5U62TJSoqyrx++PDhap381cnj+rZ37twxr5fnk3Xy/Jbk9WW9lEcn5ZR1Um5L8r5kvbxPnbx/WSf1YUnqS9ZL/emkXmWd1LMl2Q+yXvaLTvaXrJP9Z0n2r6yX/a2T40DWyXFhSY4bWS/HkW7dunVqnRxvluR4lPVyfOrkuJV1pUuXTrCtHOeyXo57nXweZJ18PizJ50fWy+dJJ58zWSefO0vyuZT18jnVyedX35+WXn/9dbVu9OjR5nVhYWHmbeW2TraRdfI/lvRt0/od8emnn6p1ffr0SdN3hJC6devviNvnTbETCphM4/KYnm5YOG3fEQ/CTP1rZVXrp3z4fpq+I+Szzu8IrS5rFfFWdbFy6Z9p+o6Q44vfERr9mAoKCnLJeYRHfEdYnEcUKlRInRu54jzC074jmjVrlqAuM/I8Qo4vOb+W82w5306N3QMWsmXLhi+//BLvvfceevbsiQoVKmD+/Plq4rz08Mwzz+DYsWMoV64cmjdvnur2ElnpUZkeIcr3hWWklh7bWv5PRpQh8evp9GhbnstTtpWrr5b1k9K2rtj3Gb2tPXWZ3PO6y763Z1sj7SNXbCvdn+RK0/Xr15E/f/70qfc8JXGzTHcUOfcb3msUhdjoKMDbx77ntVjH7wgH93061KUnb+sun+WM2taI+yi9tnX38whhhH1vCy+JLmxNN2uZZlb+Tbo+yfpx48apbFG2ppu9fPky2rVrZ/Wx119/HUOGDEmwTpqY3n33XRw6dAjLly9Xg7kTNwmdOHFCNStlz57d3L9YmpCkOcfHx0cFRDq9SUe21Q822U62l/uyPi3byutLvchryWtabitlypEjh0PbyvvWB6/LjpZ6cWRbaVqTgyet28p9vXnO19c3wf6S55Ht9H1lz7ZSfr2JUMqQeH/as60t+94Zx0lK+9MTj5Pk9qejx4nl/nT0OEluf6b1ODH6d4TUz8WLF9VrVqxYUf1veux7RN5BzU39kTUmDBdqDcetUl3s2vfeMZGourQLYuKAY13+go+vNi8GvyPsP06kLiv/3QVxJuBEt6Xwyq79DvM7gt8RPI/geYQzzyNkW9muSpUqyJkzJ5wSWPzwww8ppplN73SzEsC0b98ef/zxB6pVq2Y1sAgMDEz1DacXqfgjR46gRo0a5h1HrEtX43GZeepSyrdt2zZ1W1p307OMXrtmwHvtGJhyF0XcK3uBbPGTgtoiKhw+U0tqN4dfMAcWlAasy0z1GXcnrEvPqks5z5Yx1rYEFnalm00pzawzSZDw5JNPqh9JadIXeiYqGcydHKlwV38ZGKEMnoJ1ybo0IiMfl3qLSbqXsdGLwN7v4HX3P/jsmQm0esf2/7Uol5Hr0i2wLtOpWnlcsi6Nx8eFx6U9r2t3ViidTJa3a9cuLFmyBJs2bUJISAicRdIlVq9eHRMnTsSdO3dw4cIFvP/++yozVEBAgNNeh4iI0iBLdqD9OO32ts+B0GusRiIiSltgsXLlStUt6YUXXsAnn3yiuj5J0/tvv/3mlCqVPl/S7Ur63Hbs2FGlt5UmIHktIiIygGr/A4rXA6LDgU2TXV0aIiIygCxpGeswYsQIjBo1Cj169FADPKT/19KlS9UgbskO5Yy5LAoXLowvvvjC4echIqJ0oE+aN7cz8M+PQKOXgMKVWdVERJmY3YHFli1b0KFDB/Tp0ydB36vu3burCew2bNjglMCCiIgMrnRToHI34OQyYN044OmFri4REblKaLC2iLg4+N49DQR5yeAvbZ1fUW0hj5ameSxS6sJkb75bIiJynHz/SppZ/XaGaT8BOL1KW85vAcq2zLjXJiLj2DcX2DxF3ZShvlXlxlaLx1uNBNqMclXpyKhjLJo2bapaJX7//fcEk3IsW7ZMpYJt2ZI/KkRErsgIVbx4cbXo2aEyRMEKQL0B2u0176krlUSUCdUfAAzeDDy/yrwqtv8KbZ0s8jh5PLtbLORHS8ZSjB8/Xi2SDlYyN8lYC5nErmpVFaMSEVFm0XokcOhXIOgQcHQxULOXq0tERBlN7+oUFf5wXdEaAOeqyVTsDiyEjKeQ1K979+5V80sUKVJEpYf19/d3fgmJiChVMtepPt9P3rx5M7Y7VK6CQIs3gPXva0uVx4CsD2dzJSKizCFNgYX+wyUpZ4mIyPWkS+rBgwfV7RYtWmT8REqNhwB7vwfuXQJ2zwSaD8vY1yciIvcMLGbNmoUVK1aoLlBylcxS37598eKLLzqrfERE5A6y+gJtxwBLXgK2fgrUeRbIVcDVpSIiIiMHFps3b8bMmTPx0ksvqVmwEze3M9UsEVEmVbM3sGsGEHwE2PIR0Hmqq0tERERGDiwuXLiArl27YvDgwelTIiIick+SjUomzZv3OLD3O6DhYKAA5zUiIsos7M5J2LBhQxw/fhzR0dHpUyIiInJf5VoDFToAcTHAuvGuLg0RERm5xaJKlSoqA1SnTp1Qt25d+Pr6Jnhc5rHgoG4iokysw/vA2fXAib+Bi7uBUo1cXSIiIjJii8Xhw4fx22+/oVChQioLSURERIIlKioqfUpKRETuoUhVoM4zDyfNS5Tkg4iIPJPdLRb//POPaq2YNm1a+pSIiIjsJok0ypUrZ77tcm3eBY4sBi7vAY7/BVTr7uoSERGR0VosSpQogZiYmPQpDRERpYm3tzdKlSqlFrntcjIDb9Oh2m0ZaxHD1mwiIk9nd4uFjKuYO3cuxowZgyZNmiB37twJHi9ZsiTKli3rzDISEZE7avoasH8ucOc8sO97oPHLri4REREZKbD466+/VFYoWZYtW5bk8QEDBmDo0PirVERElCFkstLQ0FB128/PzxjdobLnBtqMBpa+DmyeCtTqA/hkdXWpiIjIKIGFBA6yEBGRcUgyDRkDJ1q0aAEfHx8YQu1ngF3fADdOajNytx7p6hIREZFRAgtx//593Lx5U423kCxQ8+bNQ3h4OHr06IHixYs7v5REROSefLJo6Wd/6QXs/hao3dfVJSIiMrbQYG0RcXHwvXsaCPLSJiHVx7DJYkB2j/CLjIxE9+7dsXjxYnV/0qRJmDVrFvbs2aMCi9u3b6dHOYmIyF1V7AiUbQnEPgA2TX64/uJOIC7WlSVzb5Z1x7ok8hz75gKzWqnF57s2qLr1JfVXX6ceNyi7A4udO3fC398fr776qmq5+PPPPzFu3DjMnz9fzcq9cuXK9CkpERG5Jxnv0WGidvv4EvNqnwW9gM+rA8f/dl3Z3JXU2YyG5rusSyIPUn8AMHgz8Pwq86rY/iu0dbLI454SWFy+fBm1atVClixZVJAh/Xpbt26tHqtYsSJbLIiIKKm7F63XSkgQsKgfgwt7gwqps9Ag1iWRJ/IrCgTUBorWfLiuaA1tnSwG7QaVpsBCxlAcOnRItVZIdyhJOZsrVy7zrNzFihVLj3ISEZE7d9lZNSKZB+Nn5V41kt2i7KpLa7OZsy6JyM0Gb7dq1Qo//PAD6tSpgxw5cqiB2+KXX37Bv//+i08//TQ9yklERO7qvx1AyNUUNjABIVeAiYUAL2+t61QSyaTPdfW2yab1tbZtMpva87yxMUCUllY4xbqUOi/bIoXtiIgMEFhIF6gff/wRZ8+eRYECBZA/f361vlGjRujcubPKn05ERBlL5q0oU6aM+bahhF2zbTtTrLaQ42Qwd+lmD7PIkMdl3yHymHSz3t7eajyFpfLlyzurTERElIbvZT2wMJzcRWzbrscPQMmHA5IfstLtx2StK5BRtkX6leHKfmCJDTOYb/xQyxxTuStQpZsWZHBywqSkjjZPUTdl5peqcmOrxeOtRgJtRqVe30SU9sCCiIjIZqWbAnkCtIHaVscGeGmPV30M8DbIxH5GVaACsGFiCnUpv+y+Wpey0KvA3tnaksMfqNQZqNwNKN8WyJYzo0tuTJJdR+olJhKY08mcfccnuzZ2lK0VRPZhYEFE5AFMJhMiIiLU7Zw5cxqrO5QEC52mapmM1HgCyxPi+HJ2msKgwll1+b9Z2twh5zcDJ5YCp1YAEbeAQwu0JWtOoEI7oPKjQGBHwDcfMi29q1NUeMLsO755XFkqIrfFwIKIyANI6u+9e/eq2y1atICPj8Gu/EtrRK95wMp3EqZJlZYKCSrkcXJuXQY+oi0y4PvSLuDEMuDkMuDeJS3gkMU7C1CmhdZdqlJXIA8zOxJR2jGwICKijCEnvOVaA1NKqruxfRbBp2J7tlSkd136SPDQXFs6TQaCDmkBhgQaN04A5zZqy/K3gBINtO5SVR4FCnDsJBFlQGAxe/ZsLFy4UE2GJ83vlgYMGIChQ4fCmfbt24cNGzbgnXfecerzEhFRBrM88S3VhEFFRteldJHTJ9lq+x5w66zWciGBxuW9D5d144BCVbSWDAk0itVKIbUuEVEaA4v9+/dj+vTpeO2111ChQgWVicRSyZLa1RNniYyMxKhRo1SzPgMLIiIiJ5JWiebDtEUGhJ9arrVkXNiqtWbIsuVjIG+p+AxTjwKlGjMgJCLnBBbHjh1Dly5dMHDgQGSEzz77DHfv3lVzZhAREVE6kfEVDQZqS+Qd4PQa4MTfwL/rgXsXgd3faEvOglomJQkyyrYCsubgLiEixe6Zc4oVK4abN28iIxw8eBDLli3D4MGDM+T1iIiICFqmqFq9gafmA++cA3rPB2r10dLWRtwEDvwE/NIL+Lg88NtzwJHFwP0QVh1RJmd3i0XdunUxbdo0jBw5Es2aNUPevHmTdIUqW7aswwWLiorC6NGj1evExMTY9D+xsbFqcQX9dV31+p6Edcm6NCKjH5dSLskMpd82rNhYNRGZdjNW3SeD16VPdiCws7bERgMXd8Dr5HJ4nVoOL8lKdexPtZh8sgFlW8JUqStMlboAuQrBbfC4ZF0aUawxvi/t+U2xO7D4+++/ERwcrJbVq1cnedyWwdsy6HvSpElWH+vUqRPat2+PGTNmoFSpUnjsscfwxx9/2FS206dPw9WOHDni6iJ4DNYl69KIjHpcSiINuSAjDh8+bKx5LCx4x0SijkXX2jiZzI3crC7zAsWeBoo+hZx3T8E/eBvyBW1DjvBLwL/r4PXvOpiWv4mw/NVxt2hz3C3WAlE5i8LIeFyyLo3I2w2/L+0OLCRwkMUROXLkUHnWrZFg4sSJE/j1119VEGOPwMBANTGUq6I5OeGoUaOG8fLHuxnWJevSiHhcOolMRLZSu1mtWjX4cCIyN6/LugD6qFuxN05prRjSmhF0AH63j6il5PFvYCpSA6bK0pLRDShcxXgZpgxRlx6CdelxdSmTr9p68T7N81iEhYXh6NGjquXC399fdZHKk8e2Nywn/48//niyj7/88suqi9XHH3+s7l++fBk3btzA8OHD8frrryebeUpO6F19Um+EMngK1iXr0oh4XDpcgaxLT63LolW1pdXbwN1LwMnlWhrb/7bD69oRtWDzFCBf2fg0to9q82Ykyi7pEkarS3fGuvS4urTnddMUWKxcuRITJkxAaGgo8uXLp7I2SdrZMWPGoGfPnnBUv379cP369QQpbiW4kFYOPz8/h5+fiMjTSFeoBw8eqNvZs2c3bFcoyiT8SwKNX9KW8FvA6ZVaGtuzG4A754EdX2lL7iKAjMeQQKNMSyBLNleXnIgcYHdgcenSJYwYMULNLdGjRw9kzZpVdRFYunQpxo0bp1ouypd3bLbOJk2aJLgvz79nz54UWzmIiDIzGbi9a9cudVsuwvCKKxlGrgJAnWe05UGYGoehWjJOrwbCrgH752pL9rxA4CNakFGhPZAtl6tLTkTpHVhs2bIFHTp0QJ8+Wp9KIT9g3bt3x4EDB9QM2Y4GFok1aNBAdbciIiIiN5Y9N1Ctu7bERAEXtmgtGadWaEHGkUXakiUHUL6tNuu3zJmRM7+rS05E6RFYZMuWfDOlNL2nR5pDGVPh7Bm9iYiIyIWk25O0TMjSdRpweS9wcqkWaEh3KQk2ZPHyAUo31Sbkk9m/85bgbiMyKLtHTDVt2lS1Svz+++/mIEKa4GUiO0kL27Jly/QoJxEREXkqGcBdqhHQ8QNg6AHgpe1A61FAkRqAKRa4sBVY+Q7wWTVgVmtgyyfAjVOuLjUROdpiUbx4cTWWYvz48WrJnz8/7ty5o8ZavPvuu6hataq9T0lERESkkcQDRatrS+uRwO3zDzNMXdwFXD2gLRsmAgUDte5SMi4joK7x0tgSZTJpygol4ynatGmDvXv34t69eyhSpAiqV6/OcRBERETkXPnLAk1f1Zaw61r3KOkudW4TcPM0sG2atuQprnWVkkCjdDPAJ80Z9YkojdL8qZN5JmSGbCIiIqIMkbswUO85bbkfApxZo7VknFkLhFwB9szSFt98QGBnrSVDBoFnTWXG4jiL8aEXdwIV2wPenMuCyF4M54mIPIAkzwgICDDfJvJ4OfIANXpoS/R94Pxm4MRSrUUj4hZw6BdtyZoTqNBOm5BP0tn6JsoyefxvbfxGPJ8FvYA8AUCnqUDVxzL+fRG5MQYWREQeQCYpDQwMdHUxiFwjaw4taJAlNga4tEvrLiWtGfcuaQGHLN5ZgLItte5S0m3q0h5gUT+ZYjLh84UEaet7zWNwQWQHBhZERETkOWRsRZnm2tJpMhB0SAswJNC4cUKb/VuW5W8CPtmSBhWKrPMCVo3UAhB2iyJKn8Bi+fLliIiIQM+ePZM8Nm/ePDVZXt++fe19WiIicoDJZEJ0dLS6LVn62B2KKD7DVEBtbWn7HnDrrNZyIYGGzJsRG5XSp0obt7F+IlCiHpAtN5DdL/5vbu2vLBwkTmR/YPHzzz+rtLL79+/HgwcPEBwcnORHbfXq1SpjFBERZSyZT2jHjh3qdosWLdRFHiJKpEB5oPkwbdkzG1gxPPUq2v5Zyo9n8X0YaKi/EnzksliXKBhJ6X7WXNqcHu6MA+EzNZsDi3z58mHjxo0ICgpCTEwMDh48mPCJsmRBo0aN0KdPn/QoJxEREZHzFKps23YBdQDvrEBUGPAgDIgK1f7GaS2EiInUlvAbzimX3hJiDk78EgUudgQrkg0rI5M5cCB8pmdzYNG1a1e1bNq0CZGRkejcuXOmrzwiIiJyU6WbatmfZKC21XEWXtrjA9dbH2MR8yBhoGEOPCxvW3nsQWii+/HbmeK059X/3xm8vB8GKolbVey6H79kyZ58oCJBBQfCZ3p2j7Fo3bq16vL066+/4qmnnjKvnz9/vhp7MWjQoExfqURERGRwEixISll1MuyVKLiIP3nuNCX5gdtyki1LrgKOl8VkAqIjEwYeUeHJBCc2BCt6YCLByoMQbQl1vJgqq5bVsSa5tAHxHAif6dkdWFy4cAGjR4/GzJkzE6xv0qQJ+vfvr7pD1axZM9NXLBERERmczFMhKWVlHotQabmIp+axmJJxqWalFSBbTm2RSQAdFRcHRIfbEZykcF+eIzoi/nljgPt3tcUu8QPh/9sBlG3h+Psjzwksdu7ciTZt2qBBgwYJ1pcrV07NxL1r1y4GFkREROQeJHgo1xqYUlLdje2zCD7uPvO2DACXVgVZ/Jw0INta9y39/vktwMH5qT/PkpeB2k8DgZ2AYrXdf6A6OR5YSApDyT6S3ARNyT1GREREZEiWQUSpJu4dVKQHqY8cebXFmjzFbQssZLLCzVO1xa8YULEjUKkzULaV1lpDbs/uULFly5ZYv369Sj8bFqb14ZPB3EuWLMHvv/+uxmAQEVHGkos+RYsWVQvnsCAilwyE18emJOGlBRKPTQeqPKql1ZWuZ//8CCx4CvioHPBLb2Df3PjB9JRpWiwCAgLw8ccfY8yYMZg4cSJy5MiB+/fvI3/+/JgwYQIqV7YxfRsRETmNtBjz+5eIDDsQvvNHWrezus9qGbUubAVOrQJOr9JaMuSvLEK6SUlLhuoyVStjU+ZSxgYWomPHjmqcxdGjR9WkeYUKFUKFChXg6+vrWGmIiIiIyLMHwks2rQrttaXLx8C1Y8DplVqgcWU/EHRQWzZNBvwCgMBH4rtMtdTm5iDPCixEVFSUam4vWbIkKlasiNu3bzOwICJyEZPJZB7jJq0X7A5FRG4xEF5aI4pW15aWbwNh14HTq7XWC0lhG3oV2D9XW2SW8/JttJYMCTb8imbceyObpGk4/sqVK9G2bVv07t0bf/31l1q3ePFiDBkyRP24ERFRxpKgYuvWrWphEg0ictuB8JJuV7pLPTUfeOc80HcxUP8FbYC4zHB+agWwdCjwaSVgVhtg80dA0GFtLhByvxaLy5cvY+zYsZg6dSpOnTqF8PBwtb5nz55YuHCh+lGTAd5ERERERGmWNQdQsYO2mD4Fgo9oLRmnVgJX/3m4bPxQCzxUl6kuQJkW2v+S8QMLmaeiXbt2qsXi33//Na/Ply+fWn/s2DEGFkRERETkPNJlqlhNbWkl4ziCLbpMbdQm4Ns3R1uy5gTKtQEqdQIqSpepItwTRg0sZGxFTEyM1cfu3bunBnITEREREaUbGV9Rr7+2REdqk/RJS4YEGzIu49RybRHF6wGBnbVAo0h1ZpkyUmDRqlUrlW522bJlKsgQISEh+PXXX9U6mc+CiIiIiChDSKYo6QYli4y1CDoU35ohXaYOaJmmZNn4AZC3ZPy2kmWqhZahilwXWBQvXhzTpk1Tc1YEBQUhW7ZsmD17Nvz9/TFp0iSVIYqIiCgB6bYgiwy+1El/6ey5Hl59ZIYXInJGl6mA2trSeoQ24d6Z1Voq23ObtDkz9n6nLTJRn2SZklS20mUqN3vdZFhgIQO1pYWiRo0aag6LZs2a4eTJkyq4KFCggJqYKXfu3A4XiIiIPJDMqLt5SoJVPj92eXin1UigzaiMLxcRebY8xYB6z2lLVITWZeq03mUqCDi5TFtkIr8S9bVUthJoFK7KLlPpGVjs2LED169fV4O3w8LC8MYbb6BmzZpqISIi15J5K/Qxboacw6L+AO3HWnLbx8Wpi1WVKlWCj3d81nO2VhBResuWUxtnIYvM+xN8KH7275Va96nLe7Vlw0QgbyltOwk0yjRnlylnBxaS9WnOnDmqK1R0dDQiIiKsbte0aVPVokFERBlHJsWrVq2acavcsqtTbCwir5mAYrUAHztz3BO7lRE5g1zUCKijLdJaGnI1PpXtKuD8ZuDeRWDPLG3Jlhso3za+y1RHIFdB7gNHA4uuXbuqrk/SciFZoW7evGl1O31eC0fJ8+zfvz/Bupw5c6J+/fpOeX4iIiK3xG5lRM6XJwCo/7y2RIUD5zY/7DIVdg048be2SJepkg0fdpkqVJldptISWOzcuVN1e5IWC2mtePHFF5Ge9u7dizfffBN16tQxrytWrBgDCyIiytzYrYwofWXLBVTuoi3SZSrowMMuU5J04tJubVk/AfAvrQUYkmmqtHSZypap947NgYVMhnf16lUULlwYoaGh6VsqQE20J92qpk+fnu6vRUTk7mJjY7F161Z1u0WLFvBhFyPPxW5lRBnbZUrmwZCl7bvAvcsWXaa2AHf/A3bP1JZsfkCFtloqW9VlqkCm21M2BxaBgYH45JNPkD9/fsTFxZl/wBJ78skn0a9fP4cLduLECZWBSgIa6RYlg/xy5OD07ERERETkInlLAA0Gaot0mZJZv1WXqTVA+HXg+F/a4uUNlGgYPwBcukxVyhRdpmwOLJo3b65aD37//Xfcv38f7du3t7pd1apVnVKw48eP48CBA1i/fr2a0VuCi88//5xdoYiIiIjI9aTLVJVu2iJdpq7+83D272vSZWqXtqwbD+Qr83D279LNAJ+sQGafIK9t27bIkyePms9CuimllQQme/bssfpY2bJlUaRIEZQrVw5PP/20ek0xefJkvPXWW1i9enWyLRfSFUAWV9Bf11Wv70lYl6xLIzL6cSnlktZk/baRGb0u3Qnr0kEyaaMMzI2OhJ6fLO7qoYcTN+YuwlTI9h+U5rpUx2dm+pwXq6MtrUerLlNeZ1bDS7pNXdgKrzsXgN3fqMWU3Q+m8u2Aip1gqtAeyJnf+vNFRz08Ls9vAyq2B7wzPpOePd/VXiaTzH1uP2lNkAHWFSpUUCf/R48eVYOrZbK81EhGqREjRlh97IknnkC3bt2SrL99+zaaNGmCRYsWoVatWgkek8Hk0nWKiCizkqDizJkz6nbFihVV+lkiSlmxUz8g4PS8ZB+/GtgPQZWeYzXawTsmEnVWdlW3D3Rejrgsvpm+/rxjIpHnxj7kvbYTea/tRtaoO+Y6McEbYfmr4V6RJrhbpCke5C6pukz5B21ByaPTke3+wyysUTkK4VL1V3C3WEuX1GmVKlVUhlanBxZTpkzBwoUL4e/vr9LQDh8+HPPnz8e8efOwZMkS+Po6dhBJECGDt2UAomXwIBmi5HUSp5zVAwsZB5LaG07PaO7IkSNqXAgHTbIujYLHZeapSynftm3bzF1XjVhGd6lLd8K6dFKLhQrOY3HmzL+oWLECvPWrwmyxsF9UOHymltRuDr8AH988ju4lz2KKA67sh9fp1fA6swpe148nfDh/OZgKVFStHcJyVIYp/l5cjx+AKo9mWJHlPPv06dM2BRZ2dYUShw8fxvLly7Fq1Sr89ddfCAkJUeul29LSpUuxZs0aPP744w7PYTFw4ED89ttv5pm9ZbC4vBkZxJ0c+YFy9Y+UEcrgKViXrEsjMvJxqbdSGLmMltylnO6AdZlG/sW1RZ+48YY3vIvX5nHp2MHI4zJFPkDpxtrSYRxw5z9tTIYMAD+/FV63z6nFGi9IW4AXfNaMBqo+mmHdouz5nrY7sJCrTO3atVPjICx5eXmhQYMGuHjxIhxVsmRJ1SVKWkKGDBmiBm/LwPHXX38dfn5+Dj8/EZGnke9gydqn3yYiIjeQrzTQaLC2PAgFds4ANk1O4R9MQMgV4L8dQNmHPXuMwu7AIleuXPjvv/+SrJceVdKa0bFjR6cU7MMPP8TixYuxZcsW9SMpg7eTy0RFRJTZSWuF3sJLRERuKLsfUKCCbdvGd+Fz+8BCTu6nTZuGsWPHquxQctK/bt06/Prrr2qcgzzmrGaX3r17q4WIiIiIyOPlLuLc7TKY3WlDcufOjV9++UV1T5JxFn/88QeGDRumWixkYLUtWaGIiIiIiCiR0k2BPAGJhm1b8gLyFNe284QWC1GiRAl88cUX6vadO3eQN29epjYkInJxdqDt27er282aNePgUyIid+TtA3SaCizqFx9cWCZvjQ82Ok1xyXwWtnA40Xm+fPkYVBARGWQuC32SPCIiclNVHwN6zUs6OaO0ZMh6edyg0tRiQURERERE6aTqY0C51sAUbU6Q2D6L4OOimbftwalZiYiIiIiMxtsiiCjVxPBBhWBgQUREREREDmNgQUREREREDmNgQUREREREDuPgbSIiD+Hv7+/qIhARUSbGwIKIyAP4+Pigdu3ari4GERFlYuwKRUREREREDmNgQUREREREDmNXKCIiDxAbG4tdu3ap240bN1Zdo4iIiDISAwsiIg8RHR3t6iIQEVEmxq5QRERERETkMAYWRERERETkMAYWRERERETkMAYWRERERETkMAYWRERERETkMGaFIiLyEH5+fq4uAhERZWIMLIiIPIDMW1GvXj1XF4OIiDIxdoUiIiIiIiKHMbAgIiIiIiKHsSsUEZEHiI2Nxd69e9XtBg0aqK5RREREGYmBBRGRh7h//76ri0BEmVVosLbERD5cF3wEyJ5Lu+1XVFvIozGwICIiIiLH7JsLbJ6SYJXPj10e3mk1EmgzirXs4RhYEBEREZFj6g8AKnVWN2Pj4nDq1ClUqlQJPt7xw3nZWpEpMLAgIiIiIsdYdnWKjUXkNRNQrJbkwmbNZiLMCkVERERERJ4bWJhMJsyePRudOnVCixYt8PLLL+PChQuuLhYREREREblTYPH111/ju+++w/jx47Fo0SLkz58fQ4YMcXWxiIgMK2fOnGohIiJyBUOOsXjw4AHmzJmDUaNGoXHjxmrdxIkTERYW5uqiEREZksxb0bBhQ1cXg4iIMjFDtlicOHECEREReOSRR8zrvL29kSdPHpeWi4iIiIiI3KjF4urVqyhQoICaRXb69Om4ffu2mkl2xIgRKFiwYIozz8riCvrruur1PQnrknVpRDwuWZdGxOOSdWlEPC6dJDYWPpZ16uJzXFt4mWSUtAsCh8cee8zqYzJIW4KHcePGoX79+iqYkCb+SZMmITQ0FL/++iu8vLwS/I+0bkgrBxFRZhUXF4eLFy+q26VKlVKtvERE5L68YyJRZ2VXdftA5+WIy+Lr0vJUqVIl1XF8LmmxKFq0KDZs2GD1sRw5cmD79u2IjIxUA7dLlChhHmPRunVr9cNZunRpq/8bGBjosoGLEs0dOXIENWrUUIEQsS6NgMdl5qlLKZ9cfBG1atUyZBndpS7dCeuSdWlEPC6dJCocWKndrFatGnx8XTMkQC7gnz592qZtXRJYpDZeQiKixPQfn5iYmGT/T7Zx9Y+UEcrgKViXrEsjMvJxqbdSGLmMltylnO6Adcm6NCIelw6y+H50ZV3a87qGbCuXFo22bdti7NixCAkJUa0XH3/8MSpXrozy5cu7unhEREREROQOgYX46KOP4O/vj+bNm6uB2zdu3MCMGTNcXSwiIiIiInKXrFDCz88P06ZNU12fZHx51qxZXV0kIiIiIiJyt8BClyWL4YtIRERERJTp8aydiMhDSFY9IiIiV2FgQUTkASRrR+PGjV1dDCIiysQMO3ibiIiIiIjcBwMLIiIiIiJyGLtCERF5yEy3Bw8eVLdr167NieeIiCjDMbAgIvIQoaGhri4CERFlYuwKRUREREREDmNgQUREREREDmNgQUREREREDmNgQUREREREDmNgQUREREREDmNWKCIiD5E1a1ZXF4GIiDIxBhZERB7Ax8cHzZo1c3UxiIgoE2NXKCIiIiIichgDCyIiIiIichi7QhEReYDY2FgcOXJE3a5Ro4bqGkVERJSRGFgQEXmIu3fvuroIRESUiTGwICIiIiIyitBgbYmJfLgu+AiQPZd226+othgQAwsiIiIiIqPYNxfYPCXBKp8fuzy802ok0GYUjIiBBRERERGRUdQfAFTqrG7GxsXh1KlTqFSpEny843MuGbS1QjCwICIiIiIyCj+Lrk6xsYi8ZgKK1ZIJi2B0TDdLREREREQOY4sFEZGH8NabyYmIiFyAgQURkQeQeStatmzp6mIQEVEmxstbRERERETkMAYWRERERETkMHaFIiLyAHFxcTh69Ki6Xb16dY63ICKiDMfAgojIA5hMJty+fdt8m4iIKKMZMrC4cOEC5s2bZ/Wxfv36oUyZMhleJiIiIiIicrPAwtfXF+XKlUuwbt26dWrmwZdeesll5SIiIiIiIjcKLIoUKYJnnnnGfP/s2bP49NNP8fXXX6Nw4cIuLRsREREREblpVqiJEyeiQ4cOaNKkiauLQkRERERE7tJiYWn79u3Yu3cv1q5dm+q2sbGxanEF/XVd9fqehHXJujQiox+XUi7JDKXfNjKj16U7YV2yLo2Ix6Vn1aU9r+1lckH6kLt37+LLL7+0+libNm3QokUL8/0XXngBBQoUwEcffZTs84WFhanxF0REmZUEFRcvXlS3S5UqxXSzRETkVJUqVULu3LmN12KRJUuWJIOzdf7+/ubbN2/exI4dOzB37twUn+/BgwdOLyMRkTvx9vZmxjwiIko3cr5tyMBCCmU5ODs5mzZtQt68edGwYcMUt5NtJAVt9uzZeZWOiIiIiMiJLeISVMj5tluPsTh48CAqV66carAgLSDSXYqIiIiIiJwrtZYKt8gKde7cOVSoUMHVxSAiIiIiIndusXj66aeTHYvhLmQkvY+Pj6uLQUTp1DycWosqUXrjcUjk2Uwmk1rc4ffG0CXs1q0bqlatCnc1a9YsNf8GpU1ISAjeeust1K1bF40aNcKkSZMQHR3N6kyDS5cuqVnr69Wrp+ry7bffxu3bt1mXDh6fHTt2xL59+1iPNrp165Y6DmvVqqXmJfr2229Zdw66ceOGyqQoE8lS2hw+fFhdyJTjsnnz5pg8eTKTwqSRHIcyhrZGjRpo27YtFi5cyMPSCRcO5PiU49IdGDqwcGfnz5/H9OnTXV0MtzZ+/HhERkaqQfzy5bRx40aeiKTxS2nIkCEoVKiQqsu//voLQUFBGDVqlPN3WiZx9epVPPfccypgI9vJhYJs2bJh69at6rP8008/qeOR0t5duF+/fiqDIqU9/f3gwYPRunVr7Nq1S2WhlO/Jzz//nFVqp6ioKLz88svqooFccPnggw/w4YcfYufOnaxLB/z888/4559/4C4YWKQDaa5699131cBzSpvQ0FCsW7cOw4cPR548eVTWr1atWuHQoUOsUjudOXNGnYCMGDECfn5+KFq0KF577TV1cic/BGSfzZs3q9ZUubpJtpNjUE4w5LtRPtM1a9bE888/jwULFrAa02Dp0qV48sknVQskpZ0ck76+viq4kL8VK1ZUFw02bNjAarXTnj17EBERoS5kSZbOpk2bonz58vzddsDly5cxY8YMVKlSBe6CgUU6RZfSZefZZ59Nj6fPFOQEWJqnZYyN1OX+/fuxevVqdOrUydVFc8sJbY4dO5Ygo8O1a9dUHcvVY7KP/GD++OOPGDduHKvODnIMSqtZkSJFzOukq6us12cMJ9tJcLZo0SIMHTqU1eaAzp07q9ZwS/L9mC9fPtarnaQb2bZt2+Dl5aV6GyxZsgT//fefag2itBkzZowKdMuWLQt3YejB20YUExNjdb0MqJFFokvpAiXBxenTpzO8fJ5QlzLYXb6YdHJV8+jRo6rVonHjxhlYQs+rSxEeHo6vv/5aXe0k++uSx2Dau5wkPlmTnOjSaiZXOW1NZUgaacEVHCvlXNJN9JdffsE777zDQy2NwsLC1EVA+czLONPSpUuzLtPgt99+w507dzBo0CA1LtJdMLCwg/RjbdmypdXHZECiXDkaO3Ys+vfvr5pTGVgk78qVK8kObJd6lPrUST9sabXQ63bFihW80p6oq9Pjjz9utS5Hjhyp+mDr7t+/j1deeQXFixfHsGHDUjvkMx0JYHv16mX1MWmh6N27d4aXydOymiReJxIHv0Su+o1/4YUXVCtGjx49uBPSSC4SSMuFBL0DBw5U3R+nTZvG+rSDtJp9+umnmDNnjpqrzZ24V2ldrGDBgjh+/HiK0aVk6JAvJrnqqTfvy213OzDSm5zYplSXcsIh9aen6s2aNas6IW7Xrp06ka5WrVoGltbYJIhNqS4tsxi9+OKLqjvKJ598wuDMiurVq9tUl2Q/aa24d+9egnVyX7rj5cqVi1VKLiWJGOS3W35j2FqRNvo5j54SNX/+/OpioFwUJPvIhayePXsiMDBQnUPqF2bcYQoDjrFwIhnsJanW6tSpowYmypeTXJmX2zJGgGx36tQpla7Osplfv82+r/aTgLdv374qIJNsJxxbQa4I2q5fv66uxFm2EPEiAbma9C6Q70cZFylJLtiCljaSiOHRRx9NsE668vA32z4SPGzZsgXff/+9On+UZdWqVaqLfXK9ZoyEgYUTffPNN+pqp77IVWH9yrzMH0D2DTiWD5OknJXmaQnYJkyYoK4mBQQEsCrtIGMqZPBX/fr1VdcouaokV0CSG0tAlB5k8GHDhg3V1UsJLiQdpTTzS857IleRi39yVV267PTp08f83Sgnd2SfRx55RP1ez5w5U7WQS8YtSSvNRDb2kRYJy3NJWaR7ntTj9u3bYXTsn5OO5KqH0ZusjFx3X331FaZMmaJSe0oaQBmT8frrr7u6aG5HMnPIvCqyJJ6sSK6KSBc/Shtrg+MpedLPWpr4ZWCnv7+/Gksln29y/HeGx2HayLwV0iVPfmtk0UnWvN27d/PQtIP8lsjFAqnH7777DoULF1ZdmKU1iBwj3cvc5XzSy5R4NB0REREREZGd2BWKiIiIiIgcxsCCiIiIiIgcxsCCiIiIiIgcxsCCiIiIiIgcxsCCiIiIiIgcxsCCiIiIiIgcxsCCiIiIiIgcxsCCiMhAbt26hQsXLqT5cSIiIldhYEFEZCDPPPMMjh07ZtPjN27cQKVKlXD58uUMK5+8Vu/evREbGwt35Yp6E8HBwep15W96ePDgAZ544gn1/oiIXIGBBRGRgcjJoa2PFypUCKdOnUKJEiWQUcaMGYMhQ4bAx8cH7soV9ZYRsmfPjueffx4TJ050dVGIKJNiYEFElIhcUZaT59q1a6N169b4+OOPERUVpR6T9e+++26C7UeNGoW333471f/Vr1jPnz9fPVajRg0MHjwYN2/eVI/36tULV65cwZtvvon3338/yX5J/LjllXf9uVetWqWeW15/5MiR6gRaWhhq1aqFZ5991vxaqZXVmr179+LSpUto1aqVuv/WW2/hlVdeSbDNlClTMHToUJuef+3atXjqqafQoEEDVb6+ffvi3Llz6jF5T1WrVlX/U69ePYwdO1atnzdvHtq2bYs6deqgZ8+e2LBhQ7LlTW5ba/WW3D4RV69exUsvvaSep2nTpvjggw8QHR2dpjoUy5YtQ/v27VG3bl1MmDDB5jqxpaxdunTBvn37cPLkyRTLQESUHhhYEBFZMJlM6kSxYMGC2LRpE3788Ud1Qi0njKJr165Yv369uSuQnGDKfVmf2v/qli5dip9//hnLly/HxYsXMXPmTLV+0aJFKF68OKZNm2Y+kbaU2uNiwYIF+O233/DTTz+p15ETTwmE5IT17t27+P777216n9ZImTt37my+L+95y5YtCAsLM6+TupBtUnt+CVAkQHrxxRexfft2/P333+p5vvjiC/NzSR3LSb28xssvv4zDhw/jq6++wtdff43du3erQEueIzw8PElZ7dk2pX0i+/eFF15Anjx51HuTk3p5bnk8LXUo5L3Kfvjjjz+wZ88eu+okpbIKaUmSoOWXX35JsQxEROnCREREZlu3bjXVr1/fFB0dbV63d+9eU7Vq1UwxMTGmiIgIU+3atU07duxQj23atMnUsGFDU1RUVKr/GxQUZAoMDDRt27bN/PjXX39t6t27t/l+mzZtTMuWLUt2j1g+fv36dfV8ly5dMj/3zp07zdt27drVNHnyZPP9Tz/91DRo0CCb3mdisbGxpgYNGqj/0z148EA9x59//qnunzx5UtWN1JG9zy+++uor09NPP61uy3tK/H5WrFihyvDff/8lWz+2bGut3pLbJ/I+atasaQoNDU3yPPa+R/219GNHrF271lSrVi2b6sSW40csXbrU1Lp161TriIjI2bKkT7hCROSe/v33X4SEhKBatWpJHpOr5yVLlkSbNm1UC0CTJk1U16MOHToga9asqf6vbCOk1UHn6+tr7lbjDMWKFTPfzpYtG4oWLWq+L6+vd7ux5X1aku5D9+7dQ6lSpRI8v1wdX7FiBbp3745169apupH3ZMvzy+PSLej06dOqu8+hQ4dU9ydLAQEB5tvSBSswMBCPPPKI6gYk3Zz+97//oXDhwklew55tU9on8j5kLEbu3LmT/I+9daiz3F66NkVGRtpVJ6kdP6VLl1bPJ88rjxMRZRQGFkREFqT7jZyQSneT5EgXoPHjx6sxDNJv//PPP7fpf/VsQFmyJPzqlS41zmLroGpb3qel0NBQ9TfxiarUhYw/kBNi6SokXYNsrYsePXqok/YWLVqoMQP//POP6kpkSYIXXc6cOVUXr/3792Pjxo1YvHixur9w4cIkA7FT2lYGOSeW3D6R9V5eXk6pQ523t3eS15H3aWudpHb8yHsXsk8YWBBRRuIYCyIiC+XKlcP58+cTDIhNTE767t+/jx9++EGd5DVs2NDm/zUKe8vq7++fIMDQSauNn5+fGij933//oWXLljY9v7RyyMm0jAWQAeByEn39+vVUgyw5ya9fv74aLC/PIXbu3OnwtskpU6aMGuRtbWxGWvf3mTNnzLcldbC0hkhLSlrrJDEJKES+fPns+j8iIkcxsCAisiAnxnIyOXr0aFy7dg1BQUEYN26c6vITExOjtpGTP+n+9M0336BTp07mVgJb/jc1crVZTiYtB0Tb87it7C1r/vz5kTdvXjVY2JK8944dO6q6aNeunbmFIbXnl8HQMphcshdFRESoQecyWFkCtuTI4HV5rbNnz6ouXTLwWbpnJe4qZO+2KZHASVoQJBOUlFcGWEsLjWQCS+v+lsHa0rVMAgxp7RowYIAKgtJSJ9bIPpLuUpatPUREGYGBBRFRohNlOUmW7irSP79bt26qi4pk8bHsgiLr5eRP0nva+78p6dOnj8oCJF2t0vK4rewtq2wnLTWSyjQx6Q4lJ9L21IWMyZDsUf3791cBibQkSOpVGVcgYwOskW5CEsjJXA2SqlXma/jwww+tjnGwZ9vU6mnGjBkqmJNAQrJLyTwY7733Xpr3twQ8jz32mNqXMhZEAhWRljqxRrp/SWsHEVFG85IR3Bn+qkRE5HYkbatcqZdB2pbjBI4fP65O4Ldu3WoeoE6uIa0zEgBJcGNvEEVE5Ci2WBARkU0aNWqkMhfJYGj9JFbGHshJrExAx6DC9WRuC2mhYVBBRK7AwIKIiGwmXYpkQjbJiCTpVmUmaukmJJO6kWs9ePAAc+fOVeM8iIhcgV2hiIiIiIjIYWyxICIiIiIihzGwICIiIiIihzGwICIiIiIihzGwICIiIiIihzGwICIiIiIihzGwICIiIiIihzGwICIiIiIihzGwICIiIiIihzGwICIiIiIiOOr/O0P1h8g+nDwAAAAASUVORK5CYII=",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "res_design = fit_survey_cs(micro_diff, \"design_weight\")\n",
+ "show_att(res_design, \"Scenario B, design weights\")\n",
+ "print(f\"true ATT (realized): {TRUE_ATT:+.2f}pp\")\n",
+ "\n",
+ "# Post-fit event-study container, keyed by event time in the nested shape\n",
+ "# the plotting cells consume ({e: {\"effect\": ..., \"se\": ...}})\n",
+ "agg_design = res_design.aggregate(\"event_study\")\n",
+ "es_design = {int(t): {\"effect\": a, \"se\": s}\n",
+ " for t, a, s in zip(agg_design.event_time, agg_design.att, agg_design.se)}\n",
+ "if HAS_MATPLOTLIB:\n",
+ " fig, ax = plt.subplots(figsize=(8, 4.5))\n",
+ " ev = sorted(es_design)\n",
+ " eff = np.array([es_design[e][\"effect\"] for e in ev]) * 100\n",
+ " se = np.array([es_design[e][\"se\"] for e in ev]) * 100\n",
+ " ax.errorbar(ev, eff, yerr=1.96 * np.nan_to_num(se), fmt=\"o-\", color=\"C1\",\n",
+ " capsize=3, label=\"design weights\")\n",
+ " ax.axhline(0, color=\"gray\", lw=0.8)\n",
+ " ax.axhline(TRUE_ATT, color=\"k\", ls=\":\", label=f\"true ATT ({TRUE_ATT:.2f}pp)\")\n",
+ " ax.axvline(-0.5, color=\"gray\", ls=\"--\", alpha=0.5)\n",
+ " ax.set_xlabel(\"event time (years since ban)\")\n",
+ " ax.set_ylabel(\"effect on smoking prevalence (pp)\")\n",
+ " ax.set_title(\"Scenario B, design weights: clean pre-trends, biased ATT\")\n",
+ " ax.legend()\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-15",
"metadata": {},
- "source": "Three things just happened, and together they are the core lesson of this tutorial:\n\n1. **The ATT is badly overstated**: about **-4.1pp against a realized truth of -2.98pp** (roughly +38%), and the 95% CI (~[-4.9, -3.4]) *excludes* the truth. You would publish \"bans cut smoking by 4 points\" with confidence.\n2. **The dynamics are fake**: the event study \"builds\" to about -5pp at event year 4. That growth is the response-rate ramp, not policy.\n3. **The pre-trends are clean** (every pre-treatment coefficient is well inside its confidence band), because the drift starts *at* adoption. A pre-trend test cannot catch at-adoption composition drift - it certifies parallel trends of *who you measured*, not of the population.\n\nThe standard toolkit passes this regression with flying colors. The composition plot above is the only thing that flagged it.\n"
+ "source": [
+ "Three things just happened, and together they are the core lesson of this tutorial:\n",
+ "\n",
+ "1. **The ATT is badly overstated**: about **-4.1pp against a realized truth of -2.98pp** (roughly +38%), and the 95% CI (~[-4.9, -3.4]) *excludes* the truth. You would publish \"bans cut smoking by 4 points\" with confidence.\n",
+ "2. **The dynamics are fake**: the event study \"builds\" to about -5pp at event year 4. That growth is the response-rate ramp, not policy.\n",
+ "3. **The pre-trends are clean** (every pre-treatment coefficient is well inside its confidence band), because the drift starts *at* adoption. A pre-trend test cannot catch at-adoption composition drift - it certifies parallel trends of *who you measured*, not of the population.\n",
+ "\n",
+ "The standard toolkit passes this regression with flying colors. The composition plot above is the only thing that flagged it.\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-16",
"metadata": {},
- "source": "## Fix attempt 1: per-wave national raking (the natural first move)\n\nThe textbook response - and the balance quickstart recipe - is to rake each survey wave to population margins: reweight so the sample's `age_band` x `educ_cat` distribution matches the ACS frame, year by year. We wrap balance's `Sample -> set_target -> adjust(method=\"rake\")` in a helper that (a) rakes within each cell of a chosen `granularity`, and (b) rescales each cell's raked weights to its population **count** - in real practice raking targets are population counts, so weight totals carry the state scale rather than balance's internal per-call normalization.\n\nOne mechanical detail: balance casts ids to `str`, so we align the returned weights back to the original rows by string id and assert nothing went missing.\n"
+ "source": [
+ "## Fix attempt 1: per-wave national raking (the natural first move)\n",
+ "\n",
+ "The textbook response - and the balance quickstart recipe - is to rake each survey wave to population margins: reweight so the sample's `age_band` x `educ_cat` distribution matches the ACS frame, year by year. We wrap balance's `Sample -> set_target -> adjust(method=\"rake\")` in a helper that (a) rakes within each cell of a chosen `granularity`, and (b) rescales each cell's raked weights to its population **count** - in real practice raking targets are population counts, so weight totals carry the state scale rather than balance's internal per-call normalization.\n",
+ "\n",
+ "One mechanical detail: balance casts ids to `str`, so we align the returned weights back to the original rows by string id and assert nothing went missing.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "cell-17",
- "metadata": {},
- "outputs": [],
- "source": "RAKE_VARS = [\"age_band\", \"educ_cat\"]\n\n\ndef rake_to_population(micro, target_df, granularity, weight_name, cell_totals):\n \"\"\"Rake design weights to population margins within each granularity cell.\n\n cell_totals maps each groupby key to the population COUNT the cell's\n raked weights must sum to.\n Returns (micro + weight_name column, {cell_key: adjusted balance Sample}).\n \"\"\"\n target_sample = Sample.from_frame(target_df, id_column=\"id\")\n cols = [\"id\", *RAKE_VARS, \"smoker\", \"design_weight\"]\n w_new = pd.Series(np.nan, index=micro.index)\n adjusted = {}\n for key, cell in micro.groupby(granularity):\n if isinstance(key, tuple) and len(key) == 1:\n key = key[0]\n s = Sample.from_frame(\n cell[cols].copy(),\n id_column=\"id\",\n weight_column=\"design_weight\",\n outcome_columns=[\"smoker\"],\n )\n adj = s.set_target(target_sample).adjust(method=\"rake\", variables=RAKE_VARS)\n w = adj.df.set_index(\"id\")[adj.weight_column]\n aligned = w.reindex(cell[\"id\"].astype(str).values).to_numpy()\n assert not np.isnan(aligned).any(), f\"NaN raked weights in cell {key}\"\n aligned = aligned * (cell_totals[key] / aligned.sum())\n w_new.loc[cell.index] = aligned\n adjusted[key] = adj\n out = micro.copy()\n out[weight_name] = w_new\n return out, adjusted\n\n\nstate_pop = truth[\"state_pop\"]\n\nmicro_diff, adj_national = rake_to_population(\n micro_diff, target, [\"year\"], \"w_national\",\n cell_totals={int(y): state_pop.sum() for y in YEARS},\n)\nres_national = fit_survey_cs(micro_diff, \"w_national\")\nshow_att(res_national, \"Scenario B, per-wave NATIONAL rake\")\n\nm24 = micro_diff[micro_diff.year == 2024].assign(\n hs=lambda d: (d.educ_cat == \"hs_or_less\").astype(float)\n)\nfor gval, label in [(0, \"never treated\"), (2020, \"2020 cohort\")]:\n sub = m24[m24.g == gval]\n share = (sub.hs * sub.w_national).sum() / sub.w_national.sum()\n print(f\"2024 hs_or_less share after national rake, {label}: \"\n f\"{share:.3f} (population: 0.350)\")\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:43.295123Z",
+ "iopub.status.busy": "2026-08-10T22:41:43.295046Z",
+ "iopub.status.idle": "2026-08-10T22:41:44.004598Z",
+ "shell.execute_reply": "2026-08-10T22:41:44.004281Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Scenario B, per-wave NATIONAL rake: ATT = -4.43pp (SE 0.48, 95% CI [-5.40, -3.47])\n",
+ "2024 hs_or_less share after national rake, never treated: 0.418 (population: 0.350)\n",
+ "2024 hs_or_less share after national rake, 2020 cohort: 0.184 (population: 0.350)\n"
+ ]
+ }
+ ],
+ "source": [
+ "RAKE_VARS = [\"age_band\", \"educ_cat\"]\n",
+ "\n",
+ "\n",
+ "def rake_to_population(micro, target_df, granularity, weight_name, cell_totals):\n",
+ " \"\"\"Rake design weights to population margins within each granularity cell.\n",
+ "\n",
+ " cell_totals maps each groupby key to the population COUNT the cell's\n",
+ " raked weights must sum to.\n",
+ " Returns (micro + weight_name column, {cell_key: adjusted balance Sample}).\n",
+ " \"\"\"\n",
+ " target_sample = Sample.from_frame(target_df, id_column=\"id\")\n",
+ " cols = [\"id\", *RAKE_VARS, \"smoker\", \"design_weight\"]\n",
+ " w_new = pd.Series(np.nan, index=micro.index)\n",
+ " adjusted = {}\n",
+ " for key, cell in micro.groupby(granularity):\n",
+ " if isinstance(key, tuple) and len(key) == 1:\n",
+ " key = key[0]\n",
+ " s = Sample.from_frame(\n",
+ " cell[cols].copy(),\n",
+ " id_column=\"id\",\n",
+ " weight_column=\"design_weight\",\n",
+ " outcome_columns=[\"smoker\"],\n",
+ " )\n",
+ " adj = s.set_target(target_sample).adjust(method=\"rake\", variables=RAKE_VARS)\n",
+ " w = adj.df.set_index(\"id\")[adj.weight_column]\n",
+ " aligned = w.reindex(cell[\"id\"].astype(str).values).to_numpy()\n",
+ " assert not np.isnan(aligned).any(), f\"NaN raked weights in cell {key}\"\n",
+ " aligned = aligned * (cell_totals[key] / aligned.sum())\n",
+ " w_new.loc[cell.index] = aligned\n",
+ " adjusted[key] = adj\n",
+ " out = micro.copy()\n",
+ " out[weight_name] = w_new\n",
+ " return out, adjusted\n",
+ "\n",
+ "\n",
+ "state_pop = truth[\"state_pop\"]\n",
+ "\n",
+ "micro_diff, adj_national = rake_to_population(\n",
+ " micro_diff, target, [\"year\"], \"w_national\",\n",
+ " cell_totals={int(y): state_pop.sum() for y in YEARS},\n",
+ ")\n",
+ "res_national = fit_survey_cs(micro_diff, \"w_national\")\n",
+ "show_att(res_national, \"Scenario B, per-wave NATIONAL rake\")\n",
+ "\n",
+ "m24 = micro_diff[micro_diff.year == 2024].assign(\n",
+ " hs=lambda d: (d.educ_cat == \"hs_or_less\").astype(float)\n",
+ ")\n",
+ "for gval, label in [(0, \"never treated\"), (2020, \"2020 cohort\")]:\n",
+ " sub = m24[m24.g == gval]\n",
+ " share = (sub.hs * sub.w_national).sum() / sub.w_national.sum()\n",
+ " print(f\"2024 hs_or_less share after national rake, {label}: \"\n",
+ " f\"{share:.3f} (population: 0.350)\")\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-18",
"metadata": {},
- "source": "**It got *worse*** - about **-4.4pp**. And the composition printout says why: after the national rake, never-treated states sit at ~0.42 low-education share while the 2020 cohort sits at ~0.18. The rake satisfied the national margin *in aggregate* - by pushing extra weight onto low-education respondents everywhere, over-correcting the states that still had them and leaving the treated states (which barely have any left) still far below the margin.\n\nRaking equalizes composition **at the level you rake at**. A DiD compares *states*; a national rake constrains only the national mixture, so the treated-vs-control composition gap - the thing biasing the DiD - survives, and the redistributed weight can even amplify it.\n\n**The rule: rake at (or below) the granularity of the units your design compares.** This is not exotic - it is what BRFSS itself does: `_LLCPWT` is raked *within each state* to state-level demographic control totals. (The balance tutorial's per-wave national rake was the right call *there*: under common drift, every state needs the same correction, so the national rake fixes each state too. Under differential drift, that shortcut collapses.)\n"
+ "source": [
+ "**It got *worse*** - about **-4.4pp**. And the composition printout says why: after the national rake, never-treated states sit at ~0.42 low-education share while the 2020 cohort sits at ~0.18. The rake satisfied the national margin *in aggregate* - by pushing extra weight onto low-education respondents everywhere, over-correcting the states that still had them and leaving the treated states (which barely have any left) still far below the margin.\n",
+ "\n",
+ "Raking equalizes composition **at the level you rake at**. A DiD compares *states*; a national rake constrains only the national mixture, so the treated-vs-control composition gap - the thing biasing the DiD - survives, and the redistributed weight can even amplify it.\n",
+ "\n",
+ "**The rule: rake at (or below) the granularity of the units your design compares.** This is not exotic - it is what BRFSS itself does: `_LLCPWT` is raked *within each state* to state-level demographic control totals. (The balance tutorial's per-wave national rake was the right call *there*: under common drift, every state needs the same correction, so the national rake fixes each state too. Under differential drift, that shortcut collapses.)\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-19",
"metadata": {},
- "source": "## Fix attempt 2: rake each state-year to the margins\n\nSame helper, `granularity=[\"state\", \"year\"]` - 350 small rakes (~750 respondents each, a couple of seconds total), each rescaled to its state's adult-population count from the ACS frame. State totals are then constant across years by construction, so the second-stage population weights recover the true state scale, uncontaminated by response-rate levels.\n"
+ "source": [
+ "## Fix attempt 2: rake each state-year to the margins\n",
+ "\n",
+ "Same helper, `granularity=[\"state\", \"year\"]` - 350 small rakes (~750 respondents each, a couple of seconds total), each rescaled to its state's adult-population count from the ACS frame. State totals are then constant across years by construction, so the second-stage population weights recover the true state scale, uncontaminated by response-rate levels.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "cell-20",
- "metadata": {},
- "outputs": [],
- "source": "micro_diff, adj_cell = rake_to_population(\n micro_diff, target, [\"state\", \"year\"], \"w_raked\",\n cell_totals={\n (st, int(y)): state_pop[st] for st in range(N_STATES) for y in YEARS\n },\n)\nres_raked = fit_survey_cs(micro_diff, \"w_raked\")\n\nshow_att(res_design, \"design weights \")\nshow_att(res_national, \"national rake \")\nshow_att(res_raked, \"state-year rake \")\nprint(f\"true ATT (realized): {TRUE_ATT:+.2f}pp\")\n\nm24 = micro_diff[micro_diff.year == 2024].assign(\n hs=lambda d: (d.educ_cat == \"hs_or_less\").astype(float)\n)\nfor gval, label in [(0, \"never treated\"), (2020, \"2020 cohort\"), (2022, \"2022 cohort\")]:\n sub = m24[m24.g == gval]\n share = (sub.hs * sub.w_raked).sum() / sub.w_raked.sum()\n print(f\"2024 hs_or_less share after state-year rake, {label}: {share:.3f}\")\n\nes_raked = res_raked.event_study_effects\nif HAS_MATPLOTLIB:\n fig, ax = plt.subplots(figsize=(8.5, 5))\n for es, color, label in [(es_design, \"C1\", \"design weights\"),\n (es_raked, \"C0\", \"state-year raked\")]:\n ev = sorted(es)\n eff = np.array([es[e][\"effect\"] for e in ev]) * 100\n se = np.array([es[e][\"se\"] for e in ev]) * 100\n ax.errorbar(ev, eff, yerr=1.96 * np.nan_to_num(se), fmt=\"o-\", color=color,\n capsize=3, label=label, alpha=0.9)\n ax.axhline(0, color=\"gray\", lw=0.8)\n ax.axhline(TRUE_ATT, color=\"k\", ls=\":\", lw=1.5,\n label=f\"true ATT ({TRUE_ATT:.2f}pp)\")\n ax.axvline(-0.5, color=\"gray\", ls=\"--\", alpha=0.5)\n ax.set_xlabel(\"event time (years since ban)\")\n ax.set_ylabel(\"effect on smoking prevalence (pp)\")\n ax.set_title(\"Composition drift manufactures dynamics; state-level raking removes them\")\n ax.legend()\n plt.tight_layout()\n plt.show()\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:44.005711Z",
+ "iopub.status.busy": "2026-08-10T22:41:44.005646Z",
+ "iopub.status.idle": "2026-08-10T22:41:50.551009Z",
+ "shell.execute_reply": "2026-08-10T22:41:50.550590Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "design weights : ATT = -4.13pp (SE 0.39, 95% CI [-4.92, -3.34])\n",
+ "national rake : ATT = -4.43pp (SE 0.48, 95% CI [-5.40, -3.47])\n",
+ "state-year rake : ATT = -3.18pp (SE 0.50, 95% CI [-4.19, -2.17])\n",
+ "true ATT (realized): -2.98pp\n",
+ "2024 hs_or_less share after state-year rake, never treated: 0.354\n",
+ "2024 hs_or_less share after state-year rake, 2020 cohort: 0.354\n",
+ "2024 hs_or_less share after state-year rake, 2022 cohort: 0.354\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "micro_diff, adj_cell = rake_to_population(\n",
+ " micro_diff, target, [\"state\", \"year\"], \"w_raked\",\n",
+ " cell_totals={\n",
+ " (st, int(y)): state_pop[st] for st in range(N_STATES) for y in YEARS\n",
+ " },\n",
+ ")\n",
+ "res_raked = fit_survey_cs(micro_diff, \"w_raked\")\n",
+ "\n",
+ "show_att(res_design, \"design weights \")\n",
+ "show_att(res_national, \"national rake \")\n",
+ "show_att(res_raked, \"state-year rake \")\n",
+ "print(f\"true ATT (realized): {TRUE_ATT:+.2f}pp\")\n",
+ "\n",
+ "m24 = micro_diff[micro_diff.year == 2024].assign(\n",
+ " hs=lambda d: (d.educ_cat == \"hs_or_less\").astype(float)\n",
+ ")\n",
+ "for gval, label in [(0, \"never treated\"), (2020, \"2020 cohort\"), (2022, \"2022 cohort\")]:\n",
+ " sub = m24[m24.g == gval]\n",
+ " share = (sub.hs * sub.w_raked).sum() / sub.w_raked.sum()\n",
+ " print(f\"2024 hs_or_less share after state-year rake, {label}: {share:.3f}\")\n",
+ "\n",
+ "agg_raked = res_raked.aggregate(\"event_study\")\n",
+ "es_raked = {int(t): {\"effect\": a, \"se\": s}\n",
+ " for t, a, s in zip(agg_raked.event_time, agg_raked.att, agg_raked.se)}\n",
+ "if HAS_MATPLOTLIB:\n",
+ " fig, ax = plt.subplots(figsize=(8.5, 5))\n",
+ " for es, color, label in [(es_design, \"C1\", \"design weights\"),\n",
+ " (es_raked, \"C0\", \"state-year raked\")]:\n",
+ " ev = sorted(es)\n",
+ " eff = np.array([es[e][\"effect\"] for e in ev]) * 100\n",
+ " se = np.array([es[e][\"se\"] for e in ev]) * 100\n",
+ " ax.errorbar(ev, eff, yerr=1.96 * np.nan_to_num(se), fmt=\"o-\", color=color,\n",
+ " capsize=3, label=label, alpha=0.9)\n",
+ " ax.axhline(0, color=\"gray\", lw=0.8)\n",
+ " ax.axhline(TRUE_ATT, color=\"k\", ls=\":\", lw=1.5,\n",
+ " label=f\"true ATT ({TRUE_ATT:.2f}pp)\")\n",
+ " ax.axvline(-0.5, color=\"gray\", ls=\"--\", alpha=0.5)\n",
+ " ax.set_xlabel(\"event time (years since ban)\")\n",
+ " ax.set_ylabel(\"effect on smoking prevalence (pp)\")\n",
+ " ax.set_title(\"Composition drift manufactures dynamics; state-level raking removes them\")\n",
+ " ax.legend()\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-21",
"metadata": {},
- "source": "Recovered: the state-year rake lands at about **-3.2pp (SE 0.5)** - within half a standard error of the realized -2.98pp truth - the fake build-up flattens onto the truth line at every horizon, and all three cohorts' low-education shares sit at 0.354 against the 0.350 population margin. The scoreboard:\n\n| weights | ATT (pp) | vs realized truth (-2.98) |\n|---|---|---|\n| design weights | ~ -4.1 | ~+38% overstated, CI excludes truth |\n| per-wave national rake | ~ -4.4 | worse - margins met in aggregate only |\n| **state-year rake, population-count totals** | **~ -3.2** | recovered (within 0.5 SE) |\n"
+ "source": [
+ "Recovered: the state-year rake lands at about **-3.2pp (SE 0.5)** - within half a standard error of the realized -2.98pp truth - the fake build-up flattens onto the truth line at every horizon, and all three cohorts' low-education shares sit at 0.354 against the 0.350 population margin. The scoreboard:\n",
+ "\n",
+ "| weights | ATT (pp) | vs realized truth (-2.98) |\n",
+ "|---|---|---|\n",
+ "| design weights | ~ -4.1 | ~+38% overstated, CI excludes truth |\n",
+ "| per-wave national rake | ~ -4.4 | worse - margins met in aggregate only |\n",
+ "| **state-year rake, population-count totals** | **~ -3.2** | recovered (within 0.5 SE) |\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-22",
"metadata": {},
- "source": "## The adapter: `balance.interop.diff_diff`\n\nEverything above used the native seam - calibrated weights as a column. balance 0.21 ships an adapter that packages the same handoff when your object in hand is a balance `Sample`:\n\n- **`bd.to_panel_for_did(sample, by=, outcomes=)`** builds the first-stage `SurveyDesign` from the Sample's *active* weight column (auto-wiring our `stratum`/`psu`/`fpc` convention columns), strips balance's bookkeeping columns, and calls `diff_diff.aggregate_survey` - returning the same `(panel, second_stage_design)` pair.\n- **`bd.fit_did(sample, estimator=, ...)`** resolves any diff-diff estimator by name (or short alias: `\"CS\"`, `\"DiD\"`, `\"BJS\"`, `\"HAD\"`), splits your kwargs between `__init__` and `fit()` by signature, forwards `survey_design=`, and attaches the source Sample to the result as `_balance_adjustment` for provenance.\n\nBoth paths must agree exactly - and we assert it:\n"
+ "source": [
+ "## The adapter: `balance.interop.diff_diff`\n",
+ "\n",
+ "Everything above used the native seam - calibrated weights as a column. balance 0.21 ships an adapter that packages the same handoff when your object in hand is a balance `Sample`:\n",
+ "\n",
+ "- **`bd.to_panel_for_did(sample, by=, outcomes=)`** builds the first-stage `SurveyDesign` from the Sample's *active* weight column (auto-wiring our `stratum`/`psu`/`fpc` convention columns), strips balance's bookkeeping columns, and calls `diff_diff.aggregate_survey` - returning the same `(panel, second_stage_design)` pair.\n",
+ "- **`bd.fit_did(sample, estimator=, ...)`** resolves any diff-diff estimator by name (or short alias: `\"CS\"`, `\"DiD\"`, `\"BJS\"`, `\"HAD\"`), splits your kwargs between `__init__` and `fit()` by signature, forwards `survey_design=`, and attaches the source Sample to the result as `_balance_adjustment` for provenance.\n",
+ "\n",
+ "Both paths must agree exactly - and we assert it:\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "cell-23",
- "metadata": {},
- "outputs": [],
- "source": "keep = [\"id\", \"state\", \"year\", \"smoker\", \"age_band\", \"educ_cat\",\n \"stratum\", \"psu\", \"fpc\", \"w_raked\"]\nsample = Sample.from_frame(\n micro_diff[keep].copy(),\n id_column=\"id\",\n weight_column=\"w_raked\",\n outcome_columns=[\"smoker\"],\n)\n\npanel_df, second_stage = bd.to_panel_for_did(\n sample, by=[\"state\", \"year\"], outcomes=\"smoker\"\n)\nprint(f\"panel: {panel_df.shape[0]} state-years, \"\n f\"second-stage weights={second_stage.weights!r}, psu={second_stage.psu!r}\")\n\n# g is unit-constant metadata: re-join it at the panel level\npanel_df = panel_df.merge(\n micro_diff[[\"state\", \"g\"]].drop_duplicates(), on=\"state\", how=\"left\"\n)\npanel_df[\"panel_id\"] = np.arange(len(panel_df))\npanel_sample = Sample.from_frame(\n panel_df,\n id_column=\"panel_id\",\n weight_column=second_stage.weights,\n outcome_columns=[\"smoker_mean\"],\n)\n\nres_adapter = bd.fit_did(\n panel_sample,\n estimator=\"CallawaySantAnna\",\n outcome=\"smoker_mean\",\n time=\"year\",\n unit=\"state\",\n treatment_first=\"g\",\n design_columns={\"psu\": \"state\"}, # match the native second-stage design\n estimation_method=\"reg\",\n control_group=\"not_yet_treated\",\n base_period=\"universal\",\n aggregate=\"all\",\n)\n\nassert np.isclose(res_adapter.overall_att, res_raked.overall_att, rtol=1e-12)\nprint(f\"native ATT: {res_raked.overall_att * 100:+.4f}pp\")\nprint(f\"adapter ATT: {res_adapter.overall_att * 100:+.4f}pp (exact match)\")\nprint(f\"provenance attached: {hasattr(res_adapter, '_balance_adjustment')}\")\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:50.552003Z",
+ "iopub.status.busy": "2026-08-10T22:41:50.551933Z",
+ "iopub.status.idle": "2026-08-10T22:41:50.909993Z",
+ "shell.execute_reply": "2026-08-10T22:41:50.909624Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "panel: 350 state-years, second-stage weights='smoker_weight', psu='state'\n",
+ "native ATT: -3.1795pp\n",
+ "adapter ATT: -3.1795pp (exact match)\n",
+ "provenance attached: True\n"
+ ]
+ }
+ ],
+ "source": [
+ "keep = [\"id\", \"state\", \"year\", \"smoker\", \"age_band\", \"educ_cat\",\n",
+ " \"stratum\", \"psu\", \"fpc\", \"w_raked\"]\n",
+ "sample = Sample.from_frame(\n",
+ " micro_diff[keep].copy(),\n",
+ " id_column=\"id\",\n",
+ " weight_column=\"w_raked\",\n",
+ " outcome_columns=[\"smoker\"],\n",
+ ")\n",
+ "\n",
+ "panel_df, second_stage = bd.to_panel_for_did(\n",
+ " sample, by=[\"state\", \"year\"], outcomes=\"smoker\"\n",
+ ")\n",
+ "print(f\"panel: {panel_df.shape[0]} state-years, \"\n",
+ " f\"second-stage weights={second_stage.weights!r}, psu={second_stage.psu!r}\")\n",
+ "\n",
+ "# g is unit-constant metadata: re-join it at the panel level\n",
+ "panel_df = panel_df.merge(\n",
+ " micro_diff[[\"state\", \"g\"]].drop_duplicates(), on=\"state\", how=\"left\"\n",
+ ")\n",
+ "panel_df[\"panel_id\"] = np.arange(len(panel_df))\n",
+ "panel_sample = Sample.from_frame(\n",
+ " panel_df,\n",
+ " id_column=\"panel_id\",\n",
+ " weight_column=second_stage.weights,\n",
+ " outcome_columns=[\"smoker_mean\"],\n",
+ ")\n",
+ "\n",
+ "res_adapter = bd.fit_did(\n",
+ " panel_sample,\n",
+ " estimator=\"CallawaySantAnna\",\n",
+ " outcome=\"smoker_mean\",\n",
+ " time=\"year\",\n",
+ " unit=\"state\",\n",
+ " treatment_first=\"g\",\n",
+ " design_columns={\"psu\": \"state\"}, # match the native second-stage design\n",
+ " estimation_method=\"reg\",\n",
+ " control_group=\"not_yet_treated\",\n",
+ " base_period=\"universal\",\n",
+ ")\n",
+ "\n",
+ "assert np.isclose(res_adapter.overall_att, res_raked.overall_att, rtol=1e-12)\n",
+ "print(f\"native ATT: {res_raked.overall_att * 100:+.4f}pp\")\n",
+ "print(f\"adapter ATT: {res_adapter.overall_att * 100:+.4f}pp (exact match)\")\n",
+ "print(f\"provenance attached: {hasattr(res_adapter, '_balance_adjustment')}\")\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-24",
"metadata": {},
- "source": "Use whichever side of the seam matches your pipeline: if you live in balance (Samples, adjustment lineage, per-wave diagnostics), the adapter keeps that lineage attached to the diff-diff result; if you live in DataFrames, the native three-liner needs no extra dependency. The contract between the packages - `aggregate_survey`'s signature, `SurveyDesign`'s fields, estimator names, the provenance side-channel - is pinned on the diff-diff side by `tests/test_balance_interop_contract.py`, so both routes stay stable.\n"
+ "source": [
+ "Use whichever side of the seam matches your pipeline: if you live in balance (Samples, adjustment lineage, per-wave diagnostics), the adapter keeps that lineage attached to the diff-diff result; if you live in DataFrames, the native three-liner needs no extra dependency. The contract between the packages - `aggregate_survey`'s signature, `SurveyDesign`'s fields, estimator names, the provenance side-channel - is pinned on the diff-diff side by `tests/test_balance_interop_contract.py`, so both routes stay stable.\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-25",
"metadata": {},
- "source": "## Does the fix depend on the estimator?\n\nA tempting escape: \"maybe a more robust estimator handles it.\" It cannot - the composition artifact lives in the *data*, in the treatment x post cell, upstream of any identification strategy. `fit_did`'s one-string estimator swap makes the sweep trivial:\n"
+ "source": [
+ "## Does the fix depend on the estimator?\n",
+ "\n",
+ "A tempting escape: \"maybe a more robust estimator handles it.\" It cannot - the composition artifact lives in the *data*, in the treatment x post cell, upstream of any identification strategy. `fit_did`'s one-string estimator swap makes the sweep trivial:\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "cell-26",
- "metadata": {},
- "outputs": [],
- "source": "ESTIMATOR_KWARGS = {\n \"CallawaySantAnna\": dict(estimation_method=\"reg\",\n control_group=\"not_yet_treated\",\n base_period=\"universal\", aggregate=\"all\"),\n \"SunAbraham\": dict(control_group=\"never_treated\"),\n \"ImputationDiD\": {},\n}\n\nrows = []\nfor wcol, wlabel in [(\"design_weight\", \"design\"), (\"w_raked\", \"state-raked\")]:\n s = Sample.from_frame(\n micro_diff[keep[:-1] + [wcol]].copy(),\n id_column=\"id\", weight_column=wcol, outcome_columns=[\"smoker\"],\n )\n pdf, ss = bd.to_panel_for_did(s, by=[\"state\", \"year\"], outcomes=\"smoker\")\n pdf = pdf.merge(micro_diff[[\"state\", \"g\"]].drop_duplicates(),\n on=\"state\", how=\"left\")\n pdf[\"panel_id\"] = np.arange(len(pdf))\n ps = Sample.from_frame(pdf, id_column=\"panel_id\", weight_column=ss.weights,\n outcome_columns=[\"smoker_mean\"])\n for name, extra in ESTIMATOR_KWARGS.items():\n r = bd.fit_did(\n ps, estimator=name, outcome=\"smoker_mean\", time=\"year\",\n unit=\"state\", treatment_first=\"g\",\n design_columns={\"psu\": \"state\"}, **extra,\n )\n rows.append({\"weights\": wlabel, \"estimator\": name,\n \"att_pp\": round(r.overall_att * 100, 2),\n \"se_pp\": round(r.overall_se * 100, 2)})\n\nsweep = pd.DataFrame(rows).pivot(index=\"estimator\", columns=\"weights\",\n values=\"att_pp\")\nprint(f\"overall ATT (pp) by estimator and weighting (realized truth: {TRUE_ATT:.2f}):\")\nprint(sweep.round(2).to_string())\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:50.910992Z",
+ "iopub.status.busy": "2026-08-10T22:41:50.910926Z",
+ "iopub.status.idle": "2026-08-10T22:41:51.655379Z",
+ "shell.execute_reply": "2026-08-10T22:41:51.655074Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "overall ATT (pp) by estimator and weighting (realized truth: -2.98):\n",
+ "weights design state-raked\n",
+ "estimator \n",
+ "CallawaySantAnna -4.13 -3.18\n",
+ "ImputationDiD -3.80 -2.86\n",
+ "SunAbraham -4.14 -3.20\n"
+ ]
+ }
+ ],
+ "source": [
+ "ESTIMATOR_KWARGS = {\n",
+ " \"CallawaySantAnna\": dict(estimation_method=\"reg\",\n",
+ " control_group=\"not_yet_treated\",\n",
+ " base_period=\"universal\"),\n",
+ " \"SunAbraham\": dict(control_group=\"never_treated\"),\n",
+ " \"ImputationDiD\": {},\n",
+ "}\n",
+ "\n",
+ "rows = []\n",
+ "for wcol, wlabel in [(\"design_weight\", \"design\"), (\"w_raked\", \"state-raked\")]:\n",
+ " s = Sample.from_frame(\n",
+ " micro_diff[keep[:-1] + [wcol]].copy(),\n",
+ " id_column=\"id\", weight_column=wcol, outcome_columns=[\"smoker\"],\n",
+ " )\n",
+ " pdf, ss = bd.to_panel_for_did(s, by=[\"state\", \"year\"], outcomes=\"smoker\")\n",
+ " pdf = pdf.merge(micro_diff[[\"state\", \"g\"]].drop_duplicates(),\n",
+ " on=\"state\", how=\"left\")\n",
+ " pdf[\"panel_id\"] = np.arange(len(pdf))\n",
+ " ps = Sample.from_frame(pdf, id_column=\"panel_id\", weight_column=ss.weights,\n",
+ " outcome_columns=[\"smoker_mean\"])\n",
+ " for name, extra in ESTIMATOR_KWARGS.items():\n",
+ " r = bd.fit_did(\n",
+ " ps, estimator=name, outcome=\"smoker_mean\", time=\"year\",\n",
+ " unit=\"state\", treatment_first=\"g\",\n",
+ " design_columns={\"psu\": \"state\"}, **extra,\n",
+ " )\n",
+ " rows.append({\"weights\": wlabel, \"estimator\": name,\n",
+ " \"att_pp\": round(r.overall_att * 100, 2),\n",
+ " \"se_pp\": round(r.overall_se * 100, 2)})\n",
+ "\n",
+ "sweep = pd.DataFrame(rows).pivot(index=\"estimator\", columns=\"weights\",\n",
+ " values=\"att_pp\")\n",
+ "print(f\"overall ATT (pp) by estimator and weighting (realized truth: {TRUE_ATT:.2f}):\")\n",
+ "print(sweep.round(2).to_string())\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-27",
"metadata": {},
- "source": "All three estimators tell the same story: roughly -3.8 to -4.2pp under design weights, roughly -2.9 to -3.2pp once the composition is fixed. Callaway-Sant'Anna's doubly-robust machinery, Sun-Abraham's interaction weighting, and Borusyak-Jaravel-Spiess's imputation efficiency all faithfully estimate the effect *in the data they are given*. Composition bias is a data problem; fix it in the data.\n"
+ "source": [
+ "All three estimators tell the same story: roughly -3.8 to -4.2pp under design weights, roughly -2.9 to -3.2pp once the composition is fixed. Callaway-Sant'Anna's doubly-robust machinery, Sun-Abraham's interaction weighting, and Borusyak-Jaravel-Spiess's imputation efficiency all faithfully estimate the effect *in the data they are given*. Composition bias is a data problem; fix it in the data.\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-28",
"metadata": {},
- "source": "## Diagnostics across the seam\n\nEach package owns half the diagnostic picture. diff-diff's `survey_metadata` describes the *second stage* - how much precision the population weighting costs across states. balance's diagnostics describe each *raking cell* - how hard the calibration had to work, which is itself an early-warning signal: the design effect of the rake blows up exactly where composition was most distorted. `bd.as_balance_diagnostic` joins the two into one flat dict.\n"
+ "source": [
+ "## Diagnostics across the seam\n",
+ "\n",
+ "Each package owns half the diagnostic picture. diff-diff's `survey_metadata` describes the *second stage* - how much precision the population weighting costs across states. balance's diagnostics describe each *raking cell* - how hard the calibration had to work, which is itself an early-warning signal: the design effect of the rake blows up exactly where composition was most distorted. `bd.as_balance_diagnostic` joins the two into one flat dict.\n"
+ ]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "cell-29",
- "metadata": {},
- "outputs": [],
- "source": "sm = res_raked.survey_metadata\nprint(f\"second stage: design_effect={sm.design_effect:.2f}, \"\n f\"effective_n={sm.effective_n:.1f} of {sm.n_psu} states, \"\n f\"df_survey={sm.df_survey}\")\n\n# per-cell raking cost: never-treated vs treated state, 2024\nnever_state = int(micro_diff.loc[micro_diff.g == 0, \"state\"].iloc[0])\ntreated_state = int(micro_diff.loc[micro_diff.g == 2020, \"state\"].iloc[0])\nfor label, st in [(\"never-treated\", never_state), (\"2020-cohort\", treated_state)]:\n diag = bd.as_balance_diagnostic(adj_cell[(st, 2024)], res_adapter)\n print(f\"\\n{label} state {st}, 2024 raking cell:\")\n print(f\" balance_design_effect: {diag['balance_design_effect']:.2f}\")\n print(f\" balance_kish_ess: {diag['balance_kish_ess']:.0f}\")\n print(f\" balance_asmd_max_post: {diag['balance_asmd_max_post']:.4f}\")\n print(f\" att (full panel fit): {diag['att'] * 100:+.2f}pp\")\n"
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-10T22:41:51.656553Z",
+ "iopub.status.busy": "2026-08-10T22:41:51.656489Z",
+ "iopub.status.idle": "2026-08-10T22:41:51.869812Z",
+ "shell.execute_reply": "2026-08-10T22:41:51.869411Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "second stage: design_effect=1.32, effective_n=37.8 of 50 states, df_survey=49\n",
+ "\n",
+ "never-treated state 0, 2024 raking cell:\n",
+ " balance_design_effect: 1.06\n",
+ " balance_kish_ess: 710\n",
+ " balance_asmd_max_post: 0.0000\n",
+ " att (full panel fit): -3.18pp\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "2020-cohort state 7, 2024 raking cell:\n",
+ " balance_design_effect: 1.93\n",
+ " balance_kish_ess: 310\n",
+ " balance_asmd_max_post: 0.0000\n",
+ " att (full panel fit): -3.18pp\n"
+ ]
+ }
+ ],
+ "source": [
+ "sm = res_raked.survey_metadata\n",
+ "print(f\"second stage: design_effect={sm.design_effect:.2f}, \"\n",
+ " f\"effective_n={sm.effective_n:.1f} of {sm.n_psu} states, \"\n",
+ " f\"df_survey={sm.df_survey}\")\n",
+ "\n",
+ "# per-cell raking cost: never-treated vs treated state, 2024\n",
+ "never_state = int(micro_diff.loc[micro_diff.g == 0, \"state\"].iloc[0])\n",
+ "treated_state = int(micro_diff.loc[micro_diff.g == 2020, \"state\"].iloc[0])\n",
+ "for label, st in [(\"never-treated\", never_state), (\"2020-cohort\", treated_state)]:\n",
+ " diag = bd.as_balance_diagnostic(adj_cell[(st, 2024)], res_adapter)\n",
+ " print(f\"\\n{label} state {st}, 2024 raking cell:\")\n",
+ " print(f\" balance_design_effect: {diag['balance_design_effect']:.2f}\")\n",
+ " print(f\" balance_kish_ess: {diag['balance_kish_ess']:.0f}\")\n",
+ " print(f\" balance_asmd_max_post: {diag['balance_asmd_max_post']:.4f}\")\n",
+ " print(f\" att (full panel fit): {diag['att'] * 100:+.2f}pp\")\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-30",
"metadata": {},
- "source": "Read the contrast: in a never-treated state the 2024 rake barely works (design effect near 1), while in a 2020-cohort state it pays a large design effect to rebuild the missing low-education mass - and post-adjustment imbalance (`asmd_max_post`) is ~0 in both, confirming the margins were hit. **Rising per-cell raking design effects concentrated in treated-post cells are the operational fingerprint of differential drift.**\n\nThe practical monitoring rule this tutorial suggests: alongside your pre-trend plot, always plot **design-weighted demographic shares by cohort over time** (the Scenario B plot) and the **per-cell raking design effects**. Both are one groupby away, and they catch what the pre-trend test structurally cannot.\n"
+ "source": [
+ "Read the contrast: in a never-treated state the 2024 rake barely works (design effect near 1), while in a 2020-cohort state it pays a large design effect to rebuild the missing low-education mass - and post-adjustment imbalance (`asmd_max_post`) is ~0 in both, confirming the margins were hit. **Rising per-cell raking design effects concentrated in treated-post cells are the operational fingerprint of differential drift.**\n",
+ "\n",
+ "The practical monitoring rule this tutorial suggests: alongside your pre-trend plot, always plot **design-weighted demographic shares by cohort over time** (the Scenario B plot) and the **per-cell raking design effects**. Both are one groupby away, and they catch what the pre-trend test structurally cannot.\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-31",
"metadata": {},
- "source": "## When do you need calibration? A checklist\n\n| Your estimand | Drift type | What you need |\n|---|---|---|\n| Descriptive (levels, trends) | any | Calibrated weights, per wave ([balance tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb)) |\n| Causal DiD | common across arms | Design weights suffice for the point estimate; calibration still improves descriptives and SEs |\n| Causal DiD | **correlated with treatment timing** | **Calibration at the granularity of your comparison units** (this tutorial) |\n\nThree caveats that decide whether raking is *enough*:\n\n- **Margins must come from a policy-unaffected source.** We raked to ACS-style demographics. If the policy itself changed the margins you rake to, calibration bakes the effect away.\n- **Raking fixes drift on observables inside your margins.** Here non-response depended only on `age_band` x `educ_cat` - missing-at-random given the margins - so raking was exact. If response depends on the *outcome itself within* demographic cells (heavier smokers refusing regardless of education), no reweighting on demographics can fix it. That regime is the subject of Sant'Anna & Xu (2023), \"Difference-in-Differences with Compositional Changes\" ([arXiv:2304.13925](https://arxiv.org/abs/2304.13925)) - a model-based route diff-diff tracks on its roadmap.\n- **Drift can pre-date adoption.** Our mechanism switched on at the effective date, which is why the pre-trends stayed clean. As an exercise, rerun with `simulate_brfss_smoking(differential=True, drift_start_offset=-2)` - drift beginning with the legislative campaign two years early - and watch the design-weight pre-treatment coefficients light up. When your event study *does* flag pre-trends in survey data, composition drift belongs on the suspect list right next to genuine trend violations.\n"
+ "source": [
+ "## When do you need calibration? A checklist\n",
+ "\n",
+ "| Your estimand | Drift type | What you need |\n",
+ "|---|---|---|\n",
+ "| Descriptive (levels, trends) | any | Calibrated weights, per wave ([balance tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb)) |\n",
+ "| Causal DiD | common across arms | Design weights suffice for the point estimate; calibration still improves descriptives and SEs |\n",
+ "| Causal DiD | **correlated with treatment timing** | **Calibration at the granularity of your comparison units** (this tutorial) |\n",
+ "\n",
+ "Three caveats that decide whether raking is *enough*:\n",
+ "\n",
+ "- **Margins must come from a policy-unaffected source.** We raked to ACS-style demographics. If the policy itself changed the margins you rake to, calibration bakes the effect away.\n",
+ "- **Raking fixes drift on observables inside your margins.** Here non-response depended only on `age_band` x `educ_cat` - missing-at-random given the margins - so raking was exact. If response depends on the *outcome itself within* demographic cells (heavier smokers refusing regardless of education), no reweighting on demographics can fix it. That regime is the subject of Sant'Anna & Xu (2023), \"Difference-in-Differences with Compositional Changes\" ([arXiv:2304.13925](https://arxiv.org/abs/2304.13925)) - a model-based route diff-diff tracks on its roadmap.\n",
+ "- **Drift can pre-date adoption.** Our mechanism switched on at the effective date, which is why the pre-trends stayed clean. As an exercise, rerun with `simulate_brfss_smoking(differential=True, drift_start_offset=-2)` - drift beginning with the legislative campaign two years early - and watch the design-weight pre-treatment coefficients light up. When your event study *does* flag pre-trends in survey data, composition drift belongs on the suspect list right next to genuine trend violations.\n"
+ ]
},
{
"cell_type": "markdown",
"id": "cell-32",
"metadata": {},
- "source": "## Summary\n\n**Key takeaways:**\n\n1. Survey weights matter for causal estimands, not just descriptive ones - but only under the right failure mode. Common non-response drift differences out of a DiD; drift **correlated with treatment timing does not** (it is treatment x post shaped, so the estimator reads it as effect).\n2. In our BRFSS-style smoking-ban setting, differential drift inflated a -2.98pp (realized) true ATT to ~-4.1pp with a fake growing dynamic profile - **while every pre-trend test stayed clean**. Pre-trend tests certify who you measured, not the population.\n3. **Raking granularity must match your comparison units.** A per-wave national rake made things *worse* (margins met in aggregate, arm-level composition untouched); raking each state-year to population-count totals - BRFSS's own practice - recovered ~-3.2pp.\n4. The diff-diff/balance seam works both ways and agrees exactly: native (`SurveyDesign` + `aggregate_survey` + `fit(survey_design=...)`) or the `balance.interop.diff_diff` adapter (`to_panel_for_did` / `fit_did`, with `_balance_adjustment` provenance).\n5. No estimator choice rescues distorted data: CS, Sun-Abraham, and ImputationDiD were all equally biased before raking and all recovered after.\n6. Monitor **demographic shares by cohort** and **per-cell raking design effects** alongside pre-trends; they are the early-warning system for composition drift.\n\n**Quick reference** - the whole workflow:\n\n```python\n# 1. rake each comparison cell to population margins (balance)\nadj = (Sample.from_frame(cell_df, id_column=\"id\", weight_column=\"design_weight\")\n .set_target(acs_sample).adjust(method=\"rake\", variables=[\"age_band\", \"educ_cat\"]))\n\n# 2. hand the calibrated weights to diff-diff (native seam)\ndesign = SurveyDesign(weights=\"w_raked\", strata=\"stratum\", psu=\"psu\", fpc=\"fpc\")\npanel, stage2 = aggregate_survey(micro, by=[\"state\", \"year\"], outcomes=\"smoker\",\n survey_design=design)\nresult = CallawaySantAnna().fit(panel, outcome=\"smoker_mean\", unit=\"state\",\n time=\"year\", first_treat=\"g\", survey_design=stage2)\n```\n\n**Related tutorials:** [Tutorial 16: Survey DiD](16_survey_did.ipynb) (SurveyDesign in depth: replicate weights, subpopulations, DEFF) - [Tutorial 17: Brand Awareness Survey](17_brand_awareness_survey.ipynb) - [Tutorial 22: HAD Survey-Weighted Workflow](22_had_survey_design.ipynb) - [balance quickstart](https://import-balance.org/docs/tutorials/quickstart/) and the [balance x diff-diff BRFSS tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb).\n\n**References:**\n\n- Callaway, B. & Sant'Anna, P. H. C. (2021). \"Difference-in-Differences with Multiple Time Periods.\" *Journal of Econometrics*, 225(2), 200-230.\n- Sant'Anna, P. H. C. & Xu, Q. (2023). \"Difference-in-Differences with Compositional Changes.\" [arXiv:2304.13925](https://arxiv.org/abs/2304.13925).\n- Deville, J.-C. & Särndal, C.-E. (1992). \"Calibration Estimators in Survey Sampling.\" *JASA*, 87(418), 376-382.\n- Deming, W. E. & Stephan, F. F. (1940). \"On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known.\" *Annals of Mathematical Statistics*, 11(4), 427-444.\n- Groves, R. M. & Peytcheva, E. (2008). \"The Impact of Nonresponse Rates on Nonresponse Bias: A Meta-Analysis.\" *Public Opinion Quarterly*, 72(2), 167-189.\n- Solon, G., Haider, S. J. & Wooldridge, J. M. (2015). \"What Are We Weighting For?\" *Journal of Human Resources*, 50(2), 301-316.\n- Lumley, T. (2004). \"Analysis of Complex Survey Samples.\" *Journal of Statistical Software*, 9(8).\n- Sarig, T., Galili, T. & Eilat, R. (2023). \"balance - a Python package for balancing biased data samples.\" [arXiv:2307.06024](https://arxiv.org/abs/2307.06024).\n"
+ "source": [
+ "## Summary\n",
+ "\n",
+ "**Key takeaways:**\n",
+ "\n",
+ "1. Survey weights matter for causal estimands, not just descriptive ones - but only under the right failure mode. Common non-response drift differences out of a DiD; drift **correlated with treatment timing does not** (it is treatment x post shaped, so the estimator reads it as effect).\n",
+ "2. In our BRFSS-style smoking-ban setting, differential drift inflated a -2.98pp (realized) true ATT to ~-4.1pp with a fake growing dynamic profile - **while every pre-trend test stayed clean**. Pre-trend tests certify who you measured, not the population.\n",
+ "3. **Raking granularity must match your comparison units.** A per-wave national rake made things *worse* (margins met in aggregate, arm-level composition untouched); raking each state-year to population-count totals - BRFSS's own practice - recovered ~-3.2pp.\n",
+ "4. The diff-diff/balance seam works both ways and agrees exactly: native (`SurveyDesign` + `aggregate_survey` + `fit(survey_design=...)`) or the `balance.interop.diff_diff` adapter (`to_panel_for_did` / `fit_did`, with `_balance_adjustment` provenance).\n",
+ "5. No estimator choice rescues distorted data: CS, Sun-Abraham, and ImputationDiD were all equally biased before raking and all recovered after.\n",
+ "6. Monitor **demographic shares by cohort** and **per-cell raking design effects** alongside pre-trends; they are the early-warning system for composition drift.\n",
+ "\n",
+ "**Quick reference** - the whole workflow:\n",
+ "\n",
+ "```python\n",
+ "# 1. rake each comparison cell to population margins (balance)\n",
+ "adj = (Sample.from_frame(cell_df, id_column=\"id\", weight_column=\"design_weight\")\n",
+ " .set_target(acs_sample).adjust(method=\"rake\", variables=[\"age_band\", \"educ_cat\"]))\n",
+ "\n",
+ "# 2. hand the calibrated weights to diff-diff (native seam)\n",
+ "design = SurveyDesign(weights=\"w_raked\", strata=\"stratum\", psu=\"psu\", fpc=\"fpc\")\n",
+ "panel, stage2 = aggregate_survey(micro, by=[\"state\", \"year\"], outcomes=\"smoker\",\n",
+ " survey_design=design)\n",
+ "result = CallawaySantAnna().fit(panel, outcome=\"smoker_mean\", unit=\"state\",\n",
+ " time=\"year\", first_treat=\"g\", survey_design=stage2)\n",
+ "```\n",
+ "\n",
+ "**Related tutorials:** [Tutorial 16: Survey DiD](16_survey_did.ipynb) (SurveyDesign in depth: replicate weights, subpopulations, DEFF) - [Tutorial 17: Brand Awareness Survey](17_brand_awareness_survey.ipynb) - [Tutorial 22: HAD Survey-Weighted Workflow](22_had_survey_design.ipynb) - [balance quickstart](https://import-balance.org/docs/tutorials/quickstart/) and the [balance x diff-diff BRFSS tutorial](https://github.com/facebookresearch/balance/blob/main/tutorials/balance_diff_diff_brfss.ipynb).\n",
+ "\n",
+ "**References:**\n",
+ "\n",
+ "- Callaway, B. & Sant'Anna, P. H. C. (2021). \"Difference-in-Differences with Multiple Time Periods.\" *Journal of Econometrics*, 225(2), 200-230.\n",
+ "- Sant'Anna, P. H. C. & Xu, Q. (2023). \"Difference-in-Differences with Compositional Changes.\" [arXiv:2304.13925](https://arxiv.org/abs/2304.13925).\n",
+ "- Deville, J.-C. & Särndal, C.-E. (1992). \"Calibration Estimators in Survey Sampling.\" *JASA*, 87(418), 376-382.\n",
+ "- Deming, W. E. & Stephan, F. F. (1940). \"On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known.\" *Annals of Mathematical Statistics*, 11(4), 427-444.\n",
+ "- Groves, R. M. & Peytcheva, E. (2008). \"The Impact of Nonresponse Rates on Nonresponse Bias: A Meta-Analysis.\" *Public Opinion Quarterly*, 72(2), 167-189.\n",
+ "- Solon, G., Haider, S. J. & Wooldridge, J. M. (2015). \"What Are We Weighting For?\" *Journal of Human Resources*, 50(2), 301-316.\n",
+ "- Lumley, T. (2004). \"Analysis of Complex Survey Samples.\" *Journal of Statistical Software*, 9(8).\n",
+ "- Sarig, T., Galili, T. & Eilat, R. (2023). \"balance - a Python package for balancing biased data samples.\" [arXiv:2307.06024](https://arxiv.org/abs/2307.06024).\n"
+ ]
}
],
"metadata": {
@@ -234,7 +1255,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.0"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/tests/test_t26_composition_drift_calibration_drift.py b/tests/test_t26_composition_drift_calibration_drift.py
index e35fcf3d..bdbf9e6d 100644
--- a/tests/test_t26_composition_drift_calibration_drift.py
+++ b/tests/test_t26_composition_drift_calibration_drift.py
@@ -243,7 +243,6 @@ def fit_survey_cs(micro, weights_col):
time="year",
first_treat="g",
survey_design=second_stage,
- aggregate="all",
)
@@ -317,8 +316,9 @@ def test_scenario_b_design_weights_overstate(pipeline):
"the realized truth (~-2.98pp)"
)
# Pre-trends stay clean (drift starts at adoption): |pre| below 1.5pp.
- es = res.event_study_effects
- max_pre = max(abs(es[e]["effect"]) * 100 for e in es if e < -1)
+ # (Post-fit container, mirroring the notebook's migrated read.)
+ agg = res.aggregate("event_study")
+ max_pre = max(abs(a) * 100 for t, a in zip(agg.event_time, agg.att) if t < -1)
assert max_pre < 1.5, f"pre-trend coefficient drifted: {max_pre:.2f}pp"
@@ -403,7 +403,6 @@ def test_native_adapter_parity(pipeline):
estimation_method="reg",
control_group="not_yet_treated",
base_period="universal",
- aggregate="all",
)
np.testing.assert_allclose(res_adapter.overall_att, pipeline["B_raked"].overall_att, rtol=1e-12)
assert hasattr(res_adapter, "_balance_adjustment")