MultiPeriodDiD: streamline fixed-effects dummy expansion

.github/workflows/ai_pr_review.yml

@@ -516,7 +516,7 @@ jobs:
           sandbox: read-only
           safety-strategy: drop-sudo
           # Recommended by OpenAI for review quality/consistency:
-          model: gpt-5.4
+          model: gpt-5.5
           effort: xhigh
 
       - name: Post PR comment (new on every event except initial open)

diff_diff/estimators.py

@@ -1604,22 +1604,9 @@ def fit(  # type: ignore[override]
                 X = np.column_stack([X, working_data[cov].values.astype(float)])
                 var_names.append(cov)
 
-        # Add fixed effects as dummy variables.
-        #
-        # MPD's design already absorbs the time dimension via non-reference
-        # period dummies (the `period_<X>` columns above) and the treatment-
-        # period interactions. If the caller passes the same column as a
-        # fixed effect (either explicitly or via the absorb -> fixed_effects
-        # auto-route for HC2/HC2-BM), the resulting `<time>_<X>` dummies
-        # would be perfectly redundant with the existing period dummies,
-        # NaN'd by `solve_ols`'s rank-deficiency handling, AND collide on
-        # name with the event-study columns in `coef_dict` (silently
-        # collapsing the dict and breaking the coefficients-vs-vcov
-        # alignment that downstream consumers rely on). Skip those FEs.
+        # Add fixed effects as dummy variables
         if fixed_effects:
             for fe in fixed_effects:
-                if fe == time:
-                    continue
                 dummies = pd.get_dummies(working_data[fe], prefix=fe, drop_first=True)
                 for col in dummies.columns:
                     X = np.column_stack([X, dummies[col].values.astype(float)])