R1 follow-up: final WLS->OLS cleanups in heterogeneity-deviation surfaces

R1 review caught three residual "WLS" references in the heterogeneity
deviation paragraphs that escaped the first R1 commit:

1. REGISTRY R-parity note still attributed the SE tolerance to "small
   WLS denominator-and-cohort-recentering numerical drift". The non-
   survey path is OLS; the recentering observation applies to the OLS
   denominator. Adjust the wording.

2. Parity-test inline comment still said "uses t-distribution with df
   = n - k from the WLS regression". Same fix.

3. TODO row still said "Either thread the WLS df into safe_inference".
   Same fix.

The legitimate "uses WLS" reference in the heterogeneity Note (under
the "Note (survey support)" sub-paragraph) is intentionally retained -
the SURVEY path IS WLS; only the non-survey heterogeneity branch is
OLS. The remaining WLS occurrence at REGISTRY.md:1271 is in an
unrelated estimator's note.

No runtime behavior change.

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

Author: igerber

TODO.md

@@ -61,7 +61,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | dCDH: Parity test SE/CI assertions only cover pure-direction scenarios; mixed-direction SE comparison is structurally apples-to-oranges (cell-count vs obs-count weighting). | `test_chaisemartin_dhaultfoeuille_parity.py` | #294 | Low |
 | dCDH by_path: negative-baseline path regression (e.g. `(-1, 0, 0, 0)`) is not yet exercised. The existing negative-D test (`test_negative_integer_D_supported`) only covers paths with negative values in non-baseline positions like `(0, -1, -1, -1)`, which does not trigger the R `substr(path, 1, 1)` bug regime (the bug needs a multi-character baseline). Add a switcher fixture with `D_{g,1} = -1` and assert the resulting path tuple key. | `tests/test_chaisemartin_dhaultfoeuille.py` | #419 | Low |
 | dCDH by_path: per-path placebo heterogeneity (`predict_het` rows for negative horizons) is currently NaN-filled in `to_dataframe(level="by_path")` `het_*` columns and unpopulated in `path_heterogeneity_effects`. R `did_multiplegt_dyn(..., by_path, predict_het)` forwards `predict_het` into each per-path `did_multiplegt_main` call alongside `placebo`, so R likely emits placebo het rows we do not yet mirror. Validate R's actual placebo predict_het output, then either implement parity or document the deviation explicitly. | `diff_diff/chaisemartin_dhaultfoeuille.py`, `diff_diff/chaisemartin_dhaultfoeuille_results.py`, `tests/test_chaisemartin_dhaultfoeuille_parity.py` | #422 | Medium |
-| dCDH heterogeneity: `_compute_heterogeneity_test` passes `df=None` to `safe_inference`, so Python uses the normal Z critical value (~1.96) for `t_stat`-derived `p_value` and `conf_int`. R `did_multiplegt_dyn(..., predict_het)` uses the t-distribution with df = n - k from the OLS regression, producing ~0.1-2% rtol gaps on CIs and p-values vs Python. Documented as a deviation in the heterogeneity R-parity Note; parity tests pin only `beta`, `se`, `t_stat`, and `n_obs`. Either thread the WLS df into `safe_inference` to match R, or formalize a separate inference-tolerance constant for the heterogeneity surface. | `diff_diff/chaisemartin_dhaultfoeuille.py`, `tests/test_chaisemartin_dhaultfoeuille_parity.py` | pilot-412 | Low |
+| dCDH heterogeneity: `_compute_heterogeneity_test` passes `df=None` to `safe_inference`, so Python uses the normal Z critical value (~1.96) for `t_stat`-derived `p_value` and `conf_int`. R `did_multiplegt_dyn(..., predict_het)` uses the t-distribution with df = n - k from the OLS regression, producing ~0.1-2% rtol gaps on CIs and p-values vs Python. Documented as a deviation in the heterogeneity R-parity Note; parity tests pin only `beta`, `se`, `t_stat`, and `n_obs`. Either thread the OLS df into `safe_inference` to match R, or formalize a separate inference-tolerance constant for the heterogeneity surface. | `diff_diff/chaisemartin_dhaultfoeuille.py`, `tests/test_chaisemartin_dhaultfoeuille_parity.py` | pilot-412 | Low |
 | CallawaySantAnna: consider materializing NaN entries for non-estimable (g,t) cells in group_time_effects dict (currently omitted with consolidated warning); would require updating downstream consumers (event study, balance_e, aggregation) | `staggered.py` | #256 | Low |
 | ImputationDiD dense `(A0'A0).toarray()` scales O((U+T+K)^2), OOM risk on large panels | `imputation.py` | #141 | Medium (deferred — only triggers when sparse solver fails) |
 | Multi-absorb weighted demeaning needs iterative alternating projections for N > 1 absorbed FE with survey weights; unweighted multi-absorb also uses single-pass (pre-existing, exact only for balanced panels) | `estimators.py` | #218 | Medium |