Add survey data support for SyntheticDiD and TROP (Phase 5)

TODO.md

@@ -54,6 +54,7 @@ Deferred items from PR reviews that were not addressed before merge.
 | 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 |
 | CallawaySantAnna survey: strata/PSU/FPC rejected at runtime. Full design-based SEs require routing the combined IF/WIF through `compute_survey_vcov()`. Currently weights-only. | `staggered.py` | #233 | Medium |
 | CallawaySantAnna survey + covariates + IPW/DR: DRDID panel nuisance-estimation IF corrections not implemented. Currently gated with NotImplementedError. Regression method with covariates works (has WLS nuisance IF correction). | `staggered.py` | #233 | Medium |
+| SyntheticDiD/TROP survey: strata/PSU/FPC deferred. Full design-based bootstrap (Rao-Wu rescaled weights) needed for survey-aware resampling. Currently pweight-only. | `synthetic_did.py`, `trop.py` | — | Medium |
 | EfficientDiD hausman_pretest() clustered covariance uses stale `n_cl` after filtering non-finite EIF rows — should recompute effective cluster count and remap indices after `row_finite` filtering | `efficient_did.py` | #230 | Medium |
 | EfficientDiD `control_group="last_cohort"` trims at `last_g - anticipation` but REGISTRY says `t >= last_g`. With `anticipation=0` (default) these are identical. With `anticipation>0`, code is arguably more conservative (excludes anticipation-contaminated periods). Either align REGISTRY with code or change code to `t < last_g` — needs design decision. | `efficient_did.py` | #230 | Low |
 | TripleDifference power: `generate_ddd_data` is a fixed 2×2×2 cross-sectional DGP — no multi-period or unbalanced-group support. Add a `generate_ddd_panel_data` for panel DDD power analysis. | `prep_dgp.py`, `power.py` | #208 | Low |

diff_diff/results.py

@@ -680,6 +680,8 @@ class SyntheticDiDResults:
     pre_treatment_fit: Optional[float] = field(default=None)
     placebo_effects: Optional[np.ndarray] = field(default=None)
     n_bootstrap: Optional[int] = field(default=None)
+    # Survey design metadata (SurveyMetadata instance from diff_diff.survey)
+    survey_metadata: Optional[Any] = field(default=None)
 
     def __repr__(self) -> str:
         """Concise string representation."""
@@ -735,6 +737,28 @@ def summary(self, alpha: Optional[float] = None) -> str:
         if self.variance_method == "bootstrap" and self.n_bootstrap is not None:
             lines.append(f"{'Bootstrap replications:':<25} {self.n_bootstrap:>10}")
 
+        # Add survey design info
+        if self.survey_metadata is not None:
+            sm = self.survey_metadata
+            lines.extend(
+                [
+                    "",
+                    "-" * 75,
+                    "Survey Design".center(75),
+                    "-" * 75,
+                    f"{'Weight type:':<25} {sm.weight_type:>10}",
+                ]
+            )
+            if sm.n_strata is not None:
+                lines.append(f"{'Strata:':<25} {sm.n_strata:>10}")
+            if sm.n_psu is not None:
+                lines.append(f"{'PSU/Cluster:':<25} {sm.n_psu:>10}")
+            lines.append(f"{'Effective sample size:':<25} {sm.effective_n:>10.1f}")
+            lines.append(f"{'Design effect (DEFF):':<25} {sm.design_effect:>10.2f}")
+            if sm.df_survey is not None:
+                lines.append(f"{'Survey d.f.:':<25} {sm.df_survey:>10}")
+            lines.append("-" * 75)
+
         lines.extend(
             [
                 "",
@@ -812,6 +836,15 @@ def to_dict(self) -> Dict[str, Any]:
         }
         if self.n_bootstrap is not None:
             result["n_bootstrap"] = self.n_bootstrap
+        if self.survey_metadata is not None:
+            sm = self.survey_metadata
+            result["weight_type"] = sm.weight_type
+            result["effective_n"] = sm.effective_n
+            result["design_effect"] = sm.design_effect
+            result["sum_weights"] = sm.sum_weights
+            result["n_strata"] = sm.n_strata
+            result["n_psu"] = sm.n_psu
+            result["df_survey"] = sm.df_survey
         return result
 
     def to_dataframe(self) -> pd.DataFrame:

diff_diff/survey.py

@@ -430,6 +430,66 @@ def _validate_unit_constant_survey(data, unit_col, survey_design):
                 )
 
 
+def _resolve_pweight_only(resolved_survey, estimator_name):
+    """Guard: reject non-pweight and strata/PSU/FPC for pweight-only estimators.
+
+    Parameters
+    ----------
+    resolved_survey : ResolvedSurveyDesign or None
+        Resolved survey design. If None, returns immediately.
+    estimator_name : str
+        Estimator name for error messages.
+
+    Raises
+    ------
+    ValueError
+        If weight_type is not 'pweight'.
+    NotImplementedError
+        If strata, PSU, or FPC are present.
+    """
+    if resolved_survey is None:
+        return
+    if resolved_survey.weight_type != "pweight":
+        raise ValueError(
+            f"{estimator_name} survey support requires weight_type='pweight'. "
+            f"Got '{resolved_survey.weight_type}'."
+        )
+    if (
+        resolved_survey.strata is not None
+        or resolved_survey.psu is not None
+        or resolved_survey.fpc is not None
+    ):
+        raise NotImplementedError(
+            f"{estimator_name} does not yet support strata/PSU/FPC in "
+            "SurveyDesign. Use SurveyDesign(weights=...) only. Full "
+            "design-based bootstrap is planned for the Bootstrap + "
+            "Survey Interaction phase."
+        )
+
+
+def _extract_unit_survey_weights(data, unit_col, survey_design, unit_order):
+    """Extract unit-level survey weights aligned to a given unit ordering.
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        Panel data with survey weight column.
+    unit_col : str
+        Unit identifier column name.
+    survey_design : SurveyDesign
+        Survey design (uses ``weights`` column name).
+    unit_order : array-like
+        Ordered sequence of unit identifiers to align weights to.
+
+    Returns
+    -------
+    np.ndarray
+        Float64 array of unit-level weights, one per unit in ``unit_order``.
+    """
+    unit_w = data.groupby(unit_col)[survey_design.weights].first()
+    return np.array([unit_w[u] for u in unit_order], dtype=np.float64)
+
+
 def _resolve_survey_for_fit(survey_design, data, inference_mode="analytical"):
     """
     Shared helper: validate and resolve a SurveyDesign for an estimator fit() call.