Remove allow_zero_se — let SE=0 produce NaN inference consistently

The allow_zero_se feature introduced P0 bugs: it couldn't distinguish
census FPC (legitimate zero variance) from single-PSU (unidentified
variance), and it created partial-NaN violations (finite CI with NaN
t_stat). Reverting to the original behavior where SE=0 always produces
all-NaN inference. Census FPC users can use analytical inference
(n_bootstrap=0) which handles this case correctly.

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

Author: igerber

diff_diff/bootstrap_utils.py

@@ -221,7 +221,6 @@ def compute_effect_bootstrap_stats(
     boot_dist: np.ndarray,
     alpha: float = 0.05,
     context: str = "bootstrap distribution",
-    allow_zero_se: bool = False,
 ) -> Tuple[float, Tuple[float, float], float]:
     """
     Compute bootstrap statistics for a single effect.
@@ -239,11 +238,6 @@ def compute_effect_bootstrap_stats(
         Significance level.
     context : str, optional
         Description for warning messages.
-    allow_zero_se : bool, default=False
-        If True, treat SE=0.0 as legitimate zero sampling variance
-        (e.g., census FPC where all bootstrap estimates are identical)
-        instead of returning NaN. When SE=0, returns
-        ``(0.0, (original_effect, original_effect), 0.0)``.
 
     Returns
     -------
@@ -284,9 +278,6 @@ def compute_effect_bootstrap_stats(
 
     # Guard: if SE is not finite or zero, all inference fields must be NaN.
     if not np.isfinite(se) or se <= 0:
-        # Census FPC: all bootstrap estimates identical → SE=0 is legitimate
-        if allow_zero_se and se == 0.0:
-            return 0.0, (original_effect, original_effect), np.nan
         warnings.warn(
             f"Bootstrap SE is non-finite or zero (n_valid={n_valid}) in {context}. "
             "Returning NaN for SE/CI/p-value.",
@@ -304,7 +295,6 @@ def compute_effect_bootstrap_stats_batch(
     original_effects: np.ndarray,
     bootstrap_matrix: np.ndarray,
     alpha: float = 0.05,
-    allow_zero_se: bool = False,
 ) -> tuple:
     """
     Batch-compute bootstrap statistics for multiple effects at once.
@@ -317,10 +307,6 @@ def compute_effect_bootstrap_stats_batch(
         Bootstrap distributions, shape (n_bootstrap, n_effects).
     alpha : float, default=0.05
         Significance level.
-    allow_zero_se : bool, default=False
-        If True, treat SE=0.0 as legitimate zero sampling variance
-        (e.g., census FPC where all bootstrap estimates are identical)
-        instead of returning NaN.
 
     Returns
     -------
@@ -399,15 +385,7 @@ def compute_effect_bootstrap_stats_batch(
 
         # Guard: SE must be positive and finite
         se_valid = np.isfinite(batch_ses) & (batch_ses > 0)
-        # Census FPC: SE=0 is legitimate zero sampling variance
-        se_zero = np.isfinite(batch_ses) & (batch_ses == 0.0)
-        if allow_zero_se and np.any(se_zero):
-            zero_idx = idx[se_zero]
-            ses[zero_idx] = 0.0
-            ci_lowers[zero_idx] = original_effects[zero_idx]
-            ci_uppers[zero_idx] = original_effects[zero_idx]
-            p_values[zero_idx] = np.nan  # p undefined when SE=0
-        n_bad_se = int(np.sum(~se_valid & ~se_zero)) if allow_zero_se else int(np.sum(~se_valid))
+        n_bad_se = int(np.sum(~se_valid))
         if n_bad_se > 0:
             warnings.warn(
                 f"{n_bad_se} effect(s) had non-finite or zero bootstrap SE. "
@@ -429,7 +407,6 @@ def compute_effect_bootstrap_stats_batch(
                 bootstrap_matrix[:, j],
                 alpha=alpha,
                 context=f"effect {j}",
-                allow_zero_se=allow_zero_se,
             )
             ses[j] = se
             ci_lowers[j] = ci[0]

diff_diff/continuous_did.py

@@ -1498,7 +1498,6 @@ def _bootstrap_gt_cell(gt, info):
                 boot_att_d[:, idx],
                 alpha=self.alpha,
                 context=f"ATT(d) at grid point {idx}",
-                allow_zero_se=_use_survey_bootstrap,
             )
             att_d_se[idx] = se
             att_d_ci_lower[idx] = ci[0]
@@ -1510,7 +1509,6 @@ def _bootstrap_gt_cell(gt, info):
                 boot_acrt_d[:, idx],
                 alpha=self.alpha,
                 context=f"ACRT(d) at grid point {idx}",
-                allow_zero_se=_use_survey_bootstrap,
             )
             acrt_d_se[idx] = se
             acrt_d_ci_lower[idx] = ci[0]
@@ -1532,7 +1530,6 @@ def _bootstrap_gt_cell(gt, info):
             boot_att_glob,
             alpha=self.alpha,
             context="overall ATT_glob",
-            allow_zero_se=_use_survey_bootstrap,
         )
         result["overall_att_se"] = se
         result["overall_att_ci"] = ci
@@ -1543,7 +1540,6 @@ def _bootstrap_gt_cell(gt, info):
             boot_acrt_glob,
             alpha=self.alpha,
             context="overall ACRT_glob",
-            allow_zero_se=_use_survey_bootstrap,
         )
         result["overall_acrt_se"] = se
         result["overall_acrt_ci"] = ci
@@ -1560,8 +1556,7 @@ def _bootstrap_gt_cell(gt, info):
                     boot_es[e],
                     alpha=self.alpha,
                     context=f"event study e={e}",
-                    allow_zero_se=_use_survey_bootstrap,
-                )
+                    )
                 es_se[e] = se_e
                 es_ci[e] = ci_e
                 es_p[e] = p_e

diff_diff/efficient_did_bootstrap.py

@@ -207,7 +207,6 @@ def _run_multiplier_bootstrap(
                 bootstrap_atts[:, j],
                 alpha=self.alpha,
                 context=f"ATT(g={gt[0]}, t={gt[1]})",
-                allow_zero_se=_use_survey_bootstrap,
             )
             gt_ses[gt] = se
             gt_cis[gt] = ci
@@ -221,7 +220,6 @@ def _run_multiplier_bootstrap(
                 bootstrap_overall,
                 alpha=self.alpha,
                 context="overall ATT",
-                allow_zero_se=_use_survey_bootstrap,
             )
 
         es_ses = es_cis = es_pvs = None
@@ -233,8 +231,7 @@ def _run_multiplier_bootstrap(
                     bootstrap_event_study[e],
                     alpha=self.alpha,
                     context=f"event study (e={e})",
-                    allow_zero_se=_use_survey_bootstrap,
-                )
+                    )
                 es_ses[e] = se
                 es_cis[e] = ci
                 es_pvs[e] = pv
@@ -248,8 +245,7 @@ def _run_multiplier_bootstrap(
                     bootstrap_group[g],
                     alpha=self.alpha,
                     context=f"group effect (g={g})",
-                    allow_zero_se=_use_survey_bootstrap,
-                )
+                    )
                 g_ses[g] = se
                 g_cis[g] = ci
                 g_pvs[g] = pv

diff_diff/imputation_bootstrap.py

@@ -349,7 +349,6 @@ def _run_bootstrap(
             boot_overall_shifted,
             alpha=self.alpha,
             context="ImputationDiD overall ATT",
-            allow_zero_se=_use_survey_bootstrap,
         )
 
         event_study_ses = None
@@ -367,7 +366,6 @@ def _run_bootstrap(
                     shifted_h,
                     alpha=self.alpha,
                     context=f"ImputationDiD event study (h={h})",
-                    allow_zero_se=_use_survey_bootstrap,
                 )
                 event_study_ses[h] = se_h
                 event_study_cis[h] = ci_h
@@ -388,7 +386,6 @@ def _run_bootstrap(
                     shifted_g,
                     alpha=self.alpha,
                     context=f"ImputationDiD group effect (g={g})",
-                    allow_zero_se=_use_survey_bootstrap,
                 )
                 group_ses[g] = se_g
                 group_cis[g] = ci_g

diff_diff/staggered_bootstrap.py

@@ -427,7 +427,7 @@ def _run_multiplier_bootstrap(
         # Batch compute bootstrap statistics for ATT(g,t)
         batch_ses, batch_ci_lo, batch_ci_hi, batch_pv = _compute_effect_bootstrap_stats_batch_func(
             original_atts, bootstrap_atts_gt, alpha=self.alpha,
-            allow_zero_se=_use_survey_bootstrap,
+
         )
         gt_ses = {}
         gt_cis = {}
@@ -445,7 +445,7 @@ def _run_multiplier_bootstrap(
         else:
             overall_se, overall_ci, overall_p_value = _compute_effect_bootstrap_stats_func(
                 original_overall, bootstrap_overall, alpha=self.alpha, context="overall ATT",
-                allow_zero_se=_use_survey_bootstrap,
+    
             )
 
         # Batch compute bootstrap statistics for event study effects
@@ -458,7 +458,7 @@ def _run_multiplier_bootstrap(
             es_boot_matrix = np.column_stack([bootstrap_event_study[e] for e in rel_periods])
             es_ses, es_ci_lo, es_ci_hi, es_pv = _compute_effect_bootstrap_stats_batch_func(
                 es_effects, es_boot_matrix, alpha=self.alpha,
-                allow_zero_se=_use_survey_bootstrap,
+    
             )
             event_study_ses = {e: float(es_ses[i]) for i, e in enumerate(rel_periods)}
             event_study_cis = {
@@ -476,7 +476,7 @@ def _run_multiplier_bootstrap(
             grp_boot_matrix = np.column_stack([bootstrap_group[g] for g in group_list])
             grp_ses, grp_ci_lo, grp_ci_hi, grp_pv = _compute_effect_bootstrap_stats_batch_func(
                 grp_effects, grp_boot_matrix, alpha=self.alpha,
-                allow_zero_se=_use_survey_bootstrap,
+    
             )
             group_effect_ses = {g: float(grp_ses[i]) for i, g in enumerate(group_list)}
             group_effect_cis = {

diff_diff/sun_abraham.py

@@ -1476,7 +1476,6 @@ def _run_rao_wu_bootstrap(
                 boot_dist,
                 alpha=self.alpha,
                 context=f"event study e={e}",
-                allow_zero_se=True,
             )
             event_study_ses[e] = se
             event_study_cis[e] = ci
@@ -1488,7 +1487,6 @@ def _run_rao_wu_bootstrap(
             bootstrap_overall,
             alpha=self.alpha,
             context="overall ATT",
-            allow_zero_se=True,
         )
 
         return SABootstrapResults(

diff_diff/two_stage_bootstrap.py

@@ -311,7 +311,6 @@ def _run_bootstrap(
             boot_overall_shifted,
             alpha=self.alpha,
             context="TwoStageDiD overall ATT",
-            allow_zero_se=_use_survey_bootstrap,
         )
 
         # --- Event study bootstrap ---
@@ -412,7 +411,6 @@ def _run_bootstrap(
                         shifted_h,
                         alpha=self.alpha,
                         context=f"TwoStageDiD event study (h={h})",
-                        allow_zero_se=_use_survey_bootstrap,
                     )
                     event_study_ses[h] = se_h
                     event_study_cis[h] = ci_h
@@ -476,7 +474,6 @@ def _run_bootstrap(
                     shifted_g,
                     alpha=self.alpha,
                     context=f"TwoStageDiD group effect (g={g})",
-                    allow_zero_se=_use_survey_bootstrap,
                 )
                 group_ses[g] = se_g
                 group_cis[g] = ci_g