Fix CI review Round 12: document Assumption 14 support-trimming behavior

P1: REGISTRY now documents that trends_nonparam does not enforce
    Assumption 14 (common last-untreated period across sets) up front.
    When within-set controls are exhausted at a given horizon, affected
    switcher/horizon pairs are excluded via the existing empty-control-pool
    mechanism. The effective estimand is trimmed to within-set support.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: igerber

docs/methodology/REGISTRY.md

@@ -613,7 +613,7 @@ Alternative: Multiplier bootstrap clustered at group via the `n_bootstrap` param
 
 - **Note (Phase 3 DID^{fd} linear trends):** Implements group-specific linear trends from Web Appendix Section 1.3 (Assumption 12, Lemma 6). Uses the Z_mat transformation: `Z[g,t] = Y[g,t] - Y[g,t-1]` (first-differenced outcomes). Since `DID_{g,l}(Z) = DID^{fd}_{g,l}` algebraically, the existing multi-horizon DID code produces trend-adjusted estimates when fed Z_mat. Requires F_g >= 3 (at least 2 pre-switch periods); groups with F_g < 3 are excluded with a `UserWarning`. Cumulated level effects `delta^{fd}_l = sum_{l'=1}^l DID^{fd}_{l'}` stored in `results.linear_trends_effects`. Cumulated SE uses conservative upper bound (sum of per-horizon SEs); cross-horizon covariance from IF vectors is a library extension (paper proves Theorem 1 per-horizon, not cross-horizon). When combined with DID^X, residualization is applied first, then first-differencing (per paper assumption ordering). **Suppressed surfaces under `trends_linear`:** `normalized_effects` (`DID^n_l`) and `cost_benefit_delta` are suppressed because they would operate on second-differences rather than level effects. Users should access cumulated level effects via `linear_trends_effects`. Activated via `trends_linear=True` in `fit()`.
 
-- **Note (Phase 3 state-set trends):** Implements state-set-specific trends from Web Appendix Section 1.4 (Assumptions 13-14). Restricts the control pool for each switcher to groups in the same set (e.g., same state in county-level data). The restriction applies in BOTH `_compute_multi_horizon_dids()` (point estimates) and `_compute_per_group_if_multi_horizon()` (influence functions) to ensure IF consistency. Cohort structure stays as `(D_{g,1}, F_g, S_g)` triples (does not incorporate set membership). Set membership must be time-invariant per group. Activated via `trends_nonparam="state_column"` in `fit()`.
+- **Note (Phase 3 state-set trends):** Implements state-set-specific trends from Web Appendix Section 1.4 (Assumptions 13-14). Restricts the control pool for each switcher to groups in the same set (e.g., same state in county-level data). The restriction applies in all four DID/IF paths: `_compute_multi_horizon_dids()`, `_compute_per_group_if_multi_horizon()`, `_compute_multi_horizon_placebos()`, and `_compute_per_group_if_placebo_horizon()`. Cohort structure stays as `(D_{g,1}, F_g, S_g)` triples (does not incorporate set membership). Set membership must be time-invariant per group. **Note on Assumption 14 (common support):** The paper requires a common last-untreated period across sets (`T_u^s` equal for all `s`). This implementation does NOT enforce Assumption 14 up front. Instead, when within-set controls are exhausted at a given horizon (because a set has shorter untreated support than others), the affected switcher/horizon pairs are silently excluded via the existing empty-control-pool mechanism. This means `N_l` may be smaller under `trends_nonparam` than without it, and the effective estimand is trimmed to the within-set support at each horizon. The existing multi-horizon A11 warning fires when exclusions occur. Activated via `trends_nonparam="state_column"` in `fit()`.
 
 - **Note (Phase 3 heterogeneity testing - partial implementation):** Partial implementation of the heterogeneity test from Web Appendix Section 1.5 (Assumption 15, Lemma 7). Computes post-treatment saturated OLS regressions of `S_g * (Y_{g, F_g-1+l} - Y_{g, F_g-1})` on a time-invariant covariate `X_g` plus cohort indicator dummies. Standard OLS inference is valid (paper shows no DID error correction needed). **Deviation from R `predict_het`:** R's full `predict_het` option additionally computes placebo regressions and a joint null test, and disallows combination with `controls`. This implementation provides only post-treatment regressions. **Rejected combinations:** `controls` (matching R), `trends_linear` (heterogeneity test uses raw level changes, incompatible with second-differenced outcomes), and `trends_nonparam` (heterogeneity test does not thread state-set control-pool restrictions). Results stored in `results.heterogeneity_effects`. Activated via `heterogeneity="covariate_column"` in `fit()`.