dCDH heterogeneity REGISTRY: drop stale "post-treatment only" claim

The top-level heterogeneity Note in REGISTRY.md still said "This
implementation provides only post-treatment regressions" — stale after
the placebo predict_het additions in this branch's earlier commits.
Updated to reflect:

1. Python now matches R on per-horizon placebo regressions (when
   `placebo=True` + `heterogeneity=` are co-set) — refers readers to
   the "Placebo predict_het" sub-note for the full contract (global +
   per-path scope, survey warn+skip behavior, R-parity classes).
2. The remaining gap from R is the joint null Wald F-test across all
   predict_het rows — Python emits per-horizon inference only.
3. The `controls` mutex stays unchanged.

Cross-references the existing render-time note in `_render_heterogeneity_section`
("Per-horizon regressions only (no joint F-test)") so the registry text
matches the user-facing summary.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: igerber

docs/methodology/REGISTRY.md

@@ -634,7 +634,7 @@ The guard is fired by `_survey_se_from_group_if` (analytical and replicate) and
 
 - **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()`. **Note (survey support):** Under `survey_design`, heterogeneity uses WLS with per-group weights `W_g = sum of obs-level survey weights in group g`, and the group-level WLS coefficient influence function is `ψ_g[X] = inv(X'WX)[1,:] @ x_g * W_g * r_g`. The group-level IF is then attributed to observation level via **one of two allocators, chosen by variance helper** so each path preserves byte-identity for its aggregation rule: (1) **Binder TSL** (`compute_survey_if_variance`) uses the **cell-period single-cell allocator** — at each horizon `l_h`, `ψ_g` is assigned in full to the post-period cell `(g, out_idx)` with `out_idx = first_switch_idx[g] - 1 + l_h` and expanded as `ψ_i = ψ_g * (w_i / W_{g, out_idx})` for obs in that cell, zero elsewhere (matches the DID_l post-period convention in the Survey IF expansion Note below). Under PSU=group per-observation distribution differs from the legacy `ψ_i = ψ_g * (w_i / W_g)`, but PSU-level aggregates telescope to the same `ψ_g` — so Binder TSL variance is byte-identical to the pre-cell-period release under PSU=group. Under within-group-varying PSU mass lands in the post-period PSU of the transition, which is what Binder TSL needs. An **empty post-period cell under zero-weight obs** (all obs at `(g, out_idx)` have `w_i = 0` despite `N > 0`) drops the group's contribution, matching the ATT cell allocator's convention; the pre-cell-period path diverged here by redistributing mass to other cells of the group. (2) **Rao-Wu replicate** (`compute_replicate_if_variance`) uses the **legacy group-level allocator** `ψ_i = ψ_g * (w_i / W_g)`. Replicate variance computes `θ_r = sum_i ratio_ir * ψ_i` at the observation level, so moving `ψ_g` mass onto the post-period cell only would silently change the replicate SE whenever a replicate column's ratios vary within group (the library accepts arbitrary per-row replicate matrices, not just PSU-aligned ones). Keeping the legacy allocator on this branch preserves byte-identity of replicate SE across every previously-supported fit; replicate + within-group-varying PSU is unreachable by construction (`SurveyDesign` rejects `replicate_weights` combined with explicit `strata/psu/fpc`). Inference uses the t-distribution with `df_survey` when provided. Under rank deficiency (any regression coefficient dropped by `solve_ols`'s R-style drop), all inference fields return NaN (conservative, matches the NaN-consistent contract). **Library extension (replicate weights):** Under a replicate-weight design (BRR/Fay/JK1/JKn/SDR), the heterogeneity regression dispatches to `compute_replicate_if_variance` (Rao-Wu weight-ratio rescaling) instead of the Binder TSL formula. The effective df is the shared `min(resolved_survey.df_survey, min(n_valid_across_sites) - 1)` used by the rest of the dCDH surfaces; if the base `df_survey` is undefined (QR-rank ≤ 1), heterogeneity inference is NaN regardless of the local `n_valid_het` (matching the dCDH top-level contract — per-site `n_valid` cannot rescue a rank-deficient design). **Library extension:** R `DIDmultiplegtDYN::predict_het` does not natively support survey weights. **Scope note (bootstrap):** Heterogeneity inference is analytical (no bootstrap path). When `n_bootstrap > 0` is combined with `heterogeneity=`, the main ATT surfaces receive bootstrap SE/CI (via the cell-level wild PSU bootstrap described in the survey + bootstrap contract Note below) while `heterogeneity_effects` continues to use the Binder TSL / Rao-Wu analytical SE described above. No gate; the two inference paths are independent.
+- **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`:** Python now matches R on per-horizon placebo regressions when the user sets `placebo=True` together with `heterogeneity=` (post-2026-05-15; see "Placebo predict_het" sub-note below for the full contract). The remaining gap is the joint null F-test that R aggregates across all `predict_het` rows — Python emits per-horizon `t_stat` / `p_value` / `conf_int` only and does NOT compute a joint Wald test across forward + placebo coefficients (tracked at REGISTRY note's "Per-horizon regressions only (no joint F-test)" rendering line). R also disallows combination with `controls`, which Python continues to enforce as an explicit `ValueError`. **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()`. **Note (survey support):** Under `survey_design`, heterogeneity uses WLS with per-group weights `W_g = sum of obs-level survey weights in group g`, and the group-level WLS coefficient influence function is `ψ_g[X] = inv(X'WX)[1,:] @ x_g * W_g * r_g`. The group-level IF is then attributed to observation level via **one of two allocators, chosen by variance helper** so each path preserves byte-identity for its aggregation rule: (1) **Binder TSL** (`compute_survey_if_variance`) uses the **cell-period single-cell allocator** — at each horizon `l_h`, `ψ_g` is assigned in full to the post-period cell `(g, out_idx)` with `out_idx = first_switch_idx[g] - 1 + l_h` and expanded as `ψ_i = ψ_g * (w_i / W_{g, out_idx})` for obs in that cell, zero elsewhere (matches the DID_l post-period convention in the Survey IF expansion Note below). Under PSU=group per-observation distribution differs from the legacy `ψ_i = ψ_g * (w_i / W_g)`, but PSU-level aggregates telescope to the same `ψ_g` — so Binder TSL variance is byte-identical to the pre-cell-period release under PSU=group. Under within-group-varying PSU mass lands in the post-period PSU of the transition, which is what Binder TSL needs. An **empty post-period cell under zero-weight obs** (all obs at `(g, out_idx)` have `w_i = 0` despite `N > 0`) drops the group's contribution, matching the ATT cell allocator's convention; the pre-cell-period path diverged here by redistributing mass to other cells of the group. (2) **Rao-Wu replicate** (`compute_replicate_if_variance`) uses the **legacy group-level allocator** `ψ_i = ψ_g * (w_i / W_g)`. Replicate variance computes `θ_r = sum_i ratio_ir * ψ_i` at the observation level, so moving `ψ_g` mass onto the post-period cell only would silently change the replicate SE whenever a replicate column's ratios vary within group (the library accepts arbitrary per-row replicate matrices, not just PSU-aligned ones). Keeping the legacy allocator on this branch preserves byte-identity of replicate SE across every previously-supported fit; replicate + within-group-varying PSU is unreachable by construction (`SurveyDesign` rejects `replicate_weights` combined with explicit `strata/psu/fpc`). Inference uses the t-distribution with `df_survey` when provided. Under rank deficiency (any regression coefficient dropped by `solve_ols`'s R-style drop), all inference fields return NaN (conservative, matches the NaN-consistent contract). **Library extension (replicate weights):** Under a replicate-weight design (BRR/Fay/JK1/JKn/SDR), the heterogeneity regression dispatches to `compute_replicate_if_variance` (Rao-Wu weight-ratio rescaling) instead of the Binder TSL formula. The effective df is the shared `min(resolved_survey.df_survey, min(n_valid_across_sites) - 1)` used by the rest of the dCDH surfaces; if the base `df_survey` is undefined (QR-rank ≤ 1), heterogeneity inference is NaN regardless of the local `n_valid_het` (matching the dCDH top-level contract — per-site `n_valid` cannot rescue a rank-deficient design). **Library extension:** R `DIDmultiplegtDYN::predict_het` does not natively support survey weights. **Scope note (bootstrap):** Heterogeneity inference is analytical (no bootstrap path). When `n_bootstrap > 0` is combined with `heterogeneity=`, the main ATT surfaces receive bootstrap SE/CI (via the cell-level wild PSU bootstrap described in the survey + bootstrap contract Note below) while `heterogeneity_effects` continues to use the Binder TSL / Rao-Wu analytical SE described above. No gate; the two inference paths are independent.
 
 - **Note (HonestDiD integration):** HonestDiD sensitivity analysis (Rambachan & Roth 2023) is available on the placebo + event study surface via `honest_did=True` in `fit()` or `compute_honest_did(results)` post-hoc. **Library extension:** dCDH HonestDiD uses `DID^{pl}_l` placebo estimates as pre-period coefficients rather than standard event-study pre-treatment coefficients. The Rambachan-Roth restrictions bound violations of the parallel trends assumption underlying the dCDH placebo estimand; interpretation differs from canonical event-study HonestDiD. A `UserWarning` is emitted at runtime. Uses diagonal variance (no full VCV available for dCDH). Relative magnitudes (DeltaRM) with Mbar=1.0 is the default when called from `fit()`, targeting the equal-weight average over all post-treatment horizons (`l_vec=None`). R's HonestDiD defaults to the first post/on-impact effect; use `compute_honest_did(results, ...)` with a custom `l_vec` to match that behavior. When `trends_linear=True`, bounds apply to the second-differenced estimand (parallel trends in first differences). Requires `L_max >= 1` for multi-horizon placebos. Gaps in the horizon grid from `trends_nonparam` support-trimming are handled by filtering to the largest consecutive block and warning.