Fix PR reference in deferred-placebo-het TODO row (#421 -> #422)

Author: igerber

TODO.md

@@ -60,7 +60,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | dCDH: Survey cell-period allocator's post-period attribution is a library convention, not derived from the observation-level survey linearization. MC coverage is empirically close to nominal on the test DGP; a formal derivation (or a covariance-aware two-cell alternative) is deferred. Documented in REGISTRY.md survey IF expansion Note. | `chaisemartin_dhaultfoeuille.py`, `docs/methodology/REGISTRY.md` | PR 2 | Medium |
 | 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` | #421 | Medium |
+| 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 |
 | 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 |