igerber · igerber · Apr 7, 2026 · Apr 7, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -7,6 +7,30 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [3.0.0] - 2026-04-07
+
+v3.0 completes the survey support roadmap: all 16 estimators (15 inference-level +
+BaconDecomposition diagnostic) now accept `survey_design`. See v2.8.0–v2.9.1 entries
+for the full feature history leading to this release.
+
+### Breaking Changes
+- **Remove `bootstrap_weight_type` parameter** from CallawaySantAnna — use `bootstrap_weights` instead (deprecated since v1.0.1)
+- **Remove TROP `method="twostep"` alias** — use `method="local"` (deprecated since v2.7.2)
+- **Remove TROP `method="joint"` alias** — use `method="global"` (deprecated since v2.7.2)
+
+### Upgrading from v2.x
+- `CallawaySantAnna(bootstrap_weight_type="mammen")` → `CallawaySantAnna(bootstrap_weights="mammen")`
+- `TROP(method="twostep")` → `TROP(method="local")`
+- `TROP(method="joint")` → `TROP(method="global")`
+
+### Deprecated
+- SyntheticDiD `lambda_reg` and `zeta` parameters formally scheduled for removal in v3.1 — use `zeta_omega`/`zeta_lambda` instead
+
+### Changed
+- Internal attribute `bootstrap_weight_type` renamed to `bootstrap_weights` in bootstrap mixin and StaggeredTripleDifference for consistency
+- TROP `set_params()` now validates `method` against `("local", "global")` — previously only validated in `__init__`
+- Documentation updated: all survey gap notes for WooldridgeDiD removed, ROADMAP Phase 10 items marked shipped
+
 ## [2.9.1] - 2026-04-06
 
 ### Added

diff --git a/ROADMAP.md b/ROADMAP.md
@@ -6,7 +6,7 @@ For past changes and release history, see [CHANGELOG.md](CHANGELOG.md).
 
 ---
 
-## Current Status (v2.9.0)
+## Current Status (v3.0)
 
 diff-diff is a **production-ready** DiD library with feature parity with R's `did` + `HonestDiD` + `synthdid` ecosystem for core DiD analysis, plus **unique survey support** that no R or Python package matches.
 
@@ -28,19 +28,17 @@ diff-diff is a **production-ready** DiD library with feature parity with R's `di
 
 ### Survey Support
 
-`SurveyDesign` with strata, PSU, FPC, weight types (pweight/fweight/aweight), lonely PSU handling. 15 of 16 estimators accept `survey_design` (WooldridgeDiD support planned for Phase 10f); design-based variance estimation varies by estimator:
+`SurveyDesign` with strata, PSU, FPC, weight types (pweight/fweight/aweight), lonely PSU handling. All 16 estimators accept `survey_design` (15 inference-level + BaconDecomposition diagnostic); design-based variance estimation varies by estimator:
 
 - **TSL variance** (Taylor Series Linearization) with strata + PSU + FPC
-- **Replicate weights**: BRR, Fay's BRR, JK1, JKn, SDR — 12 of 16 estimators (not SyntheticDiD, TROP, BaconDecomposition, or WooldridgeDiD)
+- **Replicate weights**: BRR, Fay's BRR, JK1, JKn, SDR — 12 of 16 estimators (not SyntheticDiD, TROP, BaconDecomposition, WooldridgeDiD)
 - **Survey-aware bootstrap**: multiplier at PSU (IF-based) and Rao-Wu rescaled (resampling-based)
 - **DEFF diagnostics**, **subpopulation analysis**, **weight trimming**, **CV on estimates**
 - **Repeated cross-sections**: `CallawaySantAnna(panel=False)` for BRFSS, ACS, CPS
 - **R cross-validation**: 15 tests against R's `survey` package using NHANES, RECS, and API datasets
 
 See [Survey Design Support](docs/choosing_estimator.rst#survey-design-support) for the full compatibility matrix, and [survey-roadmap.md](docs/survey-roadmap.md) for implementation details.
 
-**Gap**: WooldridgeDiD does not yet accept `survey_design`. Planned for Phase 10f.
-
 ### Infrastructure
 
 - Optional Rust backend for accelerated computation
@@ -50,24 +48,29 @@ See [Survey Design Support](docs/choosing_estimator.rst#survey-design-support) f
 
 ---
 
-## Active Work: Survey Academic Credibility (Phase 10)
+## Survey Academic Credibility (Phase 10)
 
-Before broadly announcing survey capability, we are establishing the theoretical
-and empirical foundation needed for credibility with practitioners and
-methodologists. See [survey-roadmap.md](docs/survey-roadmap.md) for detailed specs.
+Phase 10 established the theoretical and empirical foundation for survey support
+credibility. See [survey-roadmap.md](docs/survey-roadmap.md) for detailed specs.
 
 | Item | Priority | Status |
 |------|----------|--------|
-| **10a.** Theory document (`survey-theory.md`) | HIGH | Not started |
-| **10b.** Research-grade survey DGP (enhance `generate_survey_did_data`) | HIGH | Not started |
-| **10c.** Expand R validation (ImputationDiD, StackedDiD, SunAbraham, TripleDifference) | HIGH | Not started |
-| **10d.** Tutorial: flat-weight vs design-based comparison | HIGH | Not started — depends on 10b |
+| **10a.** Theory document (`survey-theory.md`) | HIGH | ✅ Shipped (v2.9.1) |
+| **10b.** Research-grade survey DGP (enhance `generate_survey_did_data`) | HIGH | ✅ Shipped (v2.9.1) |
+| **10c.** Expand R validation (ImputationDiD, StackedDiD, SunAbraham, TripleDifference) | HIGH | ✅ Shipped (v2.9.1) |
+| **10d.** Tutorial: flat-weight vs design-based comparison | HIGH | ✅ Shipped (v2.9.1) |
 | **10e.** Position paper / arXiv preprint | MEDIUM | Not started — depends on 10b |
-| **10f.** WooldridgeDiD survey support (OLS + logit + Poisson) | MEDIUM | Not started |
+| **10f.** WooldridgeDiD survey support (OLS + logit + Poisson) | MEDIUM | ✅ Shipped (v2.9.0) |
 | **10g.** Practitioner guidance: when does survey design matter? | LOW | Not started |
 
 ---
 
+## Future: Survey Aggregation Helper
+
+**`survey_aggregate()` helper function** for the microdata-to-panel workflow. Bridges individual-level survey data (BRFSS, ACS, CPS) collected as repeated cross-sections to geographic-level (state, city) panel DiD. Computes design-based cell means and precision weights that estimators can consume directly.
+
+---
+
 ## Future Estimators
 
 ### de Chaisemartin-D'Haultfouille Estimator

diff --git a/TODO.md b/TODO.md
@@ -116,9 +116,9 @@ Mypy reports 0 errors. All mixin `attr-defined` errors resolved via
 
 Deprecated parameters still present for backward compatibility:
 
-- `bootstrap_weight_type` in `CallawaySantAnna` (`staggered.py`)
-  - Deprecated in favor of `bootstrap_weights` parameter
-  - Remove in next major version (v3.0)
+- `lambda_reg` and `zeta` in `SyntheticDiD` (`synthetic_did.py`)
+  - Deprecated in favor of `zeta_omega`/`zeta_lambda` parameters
+  - Remove in v3.1
 
 ---
 

diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py
@@ -214,7 +214,7 @@
 EDiD = EfficientDiD
 ETWFE = WooldridgeDiD
 
-__version__ = "2.9.1"
+__version__ = "3.0.0"
 __all__ = [
     # Estimators
     "DifferenceInDifferences",

diff --git a/diff_diff/staggered.py b/diff_diff/staggered.py
@@ -153,9 +153,6 @@ class CallawaySantAnna(
         - "rademacher": +1/-1 with equal probability (standard choice)
         - "mammen": Two-point distribution (asymptotically valid, matches skewness)
         - "webb": Six-point distribution (recommended when n_clusters < 20)
-    bootstrap_weight_type : str, optional
-        .. deprecated:: 1.0.1
-            Use ``bootstrap_weights`` instead. Will be removed in v3.0.
     seed : int, optional
         Random seed for reproducibility.
     rank_deficient_action : str, default="warn"
@@ -293,7 +290,6 @@ def __init__(
         cluster: Optional[str] = None,
         n_bootstrap: int = 0,
         bootstrap_weights: Optional[str] = None,
-        bootstrap_weight_type: Optional[str] = None,
         seed: Optional[int] = None,
         rank_deficient_action: str = "warn",
         base_period: str = "varying",
@@ -323,18 +319,7 @@ def __init__(
                 f"pscore_fallback must be 'error' or 'unconditional', " f"got '{pscore_fallback}'"
             )
 
-        # Handle bootstrap_weight_type deprecation
-        if bootstrap_weight_type is not None:
-            warnings.warn(
-                "bootstrap_weight_type is deprecated and will be removed in v3.0. "
-                "Use bootstrap_weights instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            if bootstrap_weights is None:
-                bootstrap_weights = bootstrap_weight_type
-
-        # Default to rademacher if neither specified
+        # Default to rademacher if not specified
         if bootstrap_weights is None:
             bootstrap_weights = "rademacher"
 
@@ -362,8 +347,6 @@ def __init__(
         self.cluster = cluster
         self.n_bootstrap = n_bootstrap
         self.bootstrap_weights = bootstrap_weights
-        # Keep bootstrap_weight_type for backward compatibility
-        self.bootstrap_weight_type = bootstrap_weights
         self.seed = seed
         self.rank_deficient_action = rank_deficient_action
         self.base_period = base_period
@@ -3881,8 +3864,6 @@ def get_params(self) -> Dict[str, Any]:
             "cluster": self.cluster,
             "n_bootstrap": self.n_bootstrap,
             "bootstrap_weights": self.bootstrap_weights,
-            # Deprecated but kept for backward compatibility
-            "bootstrap_weight_type": self.bootstrap_weight_type,
             "seed": self.seed,
             "rank_deficient_action": self.rank_deficient_action,
             "base_period": self.base_period,

diff --git a/diff_diff/staggered_bootstrap.py b/diff_diff/staggered_bootstrap.py
@@ -118,7 +118,7 @@ class CallawaySantAnnaBootstrapMixin:
 
     # Type hints for attributes accessed from the main class
     n_bootstrap: int
-    bootstrap_weight_type: str
+    bootstrap_weights: str
     alpha: float
     seed: Optional[int]
     anticipation: int
@@ -329,7 +329,7 @@ def _run_multiplier_bootstrap(
         if _use_survey_bootstrap:
             # PSU-level multiplier weights
             psu_weights, psu_ids = _generate_survey_multiplier_weights_batch(
-                self.n_bootstrap, resolved_survey_unit, self.bootstrap_weight_type, rng
+                self.n_bootstrap, resolved_survey_unit, self.bootstrap_weights, rng
             )
             # Build unit → PSU column map
             if resolved_survey_unit.psu is not None:
@@ -348,7 +348,7 @@ def _run_multiplier_bootstrap(
         else:
             # Standard unit-level weights (no survey or weights-only)
             all_bootstrap_weights = _generate_bootstrap_weights_batch(
-                self.n_bootstrap, n_units, self.bootstrap_weight_type, rng
+                self.n_bootstrap, n_units, self.bootstrap_weights, rng
             )
 
         # Vectorized bootstrap ATT(g,t) computation
@@ -534,7 +534,7 @@ def _run_multiplier_bootstrap(
 
         return CSBootstrapResults(
             n_bootstrap=self.n_bootstrap,
-            weight_type=self.bootstrap_weight_type,
+            weight_type=self.bootstrap_weights,
             alpha=self.alpha,
             overall_att_se=overall_se,
             overall_att_ci=overall_ci,

diff --git a/diff_diff/staggered_triple_diff.py b/diff_diff/staggered_triple_diff.py
@@ -147,7 +147,6 @@ def __init__(
         self.base_period = base_period
         self.n_bootstrap = n_bootstrap
         self.bootstrap_weights = bootstrap_weights
-        self.bootstrap_weight_type = bootstrap_weights
         self.seed = seed
         self.cband = cband
         self.pscore_trim = pscore_trim
@@ -186,7 +185,7 @@ def set_params(self, **params) -> "StaggeredTripleDifference":
                 raise ValueError(f"Unknown parameter: {key}")
             setattr(self, key, value)
         if "bootstrap_weights" in params:
-            self.bootstrap_weight_type = params["bootstrap_weights"]
+            self.bootstrap_weights = params["bootstrap_weights"]
         return self
 
     # ------------------------------------------------------------------

diff --git a/diff_diff/synthetic_did.py b/diff_diff/synthetic_did.py
@@ -144,14 +144,14 @@ def __init__(
             warnings.warn(
                 "lambda_reg is deprecated and ignored. Regularization is now "
                 "auto-computed from data. Use zeta_omega to override unit weight "
-                "regularization.",
+                "regularization. Will be removed in v3.1.",
                 DeprecationWarning,
                 stacklevel=2,
             )
         if zeta is not None:
             warnings.warn(
                 "zeta is deprecated and ignored. Use zeta_lambda to override "
-                "time weight regularization.",
+                "time weight regularization. Will be removed in v3.1.",
                 DeprecationWarning,
                 stacklevel=2,
             )
@@ -1124,7 +1124,8 @@ def set_params(self, **params) -> "SyntheticDiD":
         for key, value in params.items():
             if key in _deprecated:
                 warnings.warn(
-                    f"{key} is deprecated and ignored. Use zeta_omega/zeta_lambda " f"instead.",
+                    f"{key} is deprecated and ignored. Use zeta_omega/zeta_lambda "
+                    f"instead. Will be removed in v3.1.",
                     DeprecationWarning,
                     stacklevel=2,
                 )

diff --git a/diff_diff/trop.py b/diff_diff/trop.py
@@ -77,10 +77,6 @@ class TROP(TROPLocalMixin, TROPGlobalMixin):
           ATT is the mean of these effects. For the paper's full
           per-treated-cell estimator, use ``method='local'``.
 
-        - 'twostep': Deprecated alias for 'local'. Will be removed in v3.0.
-
-        - 'joint': Deprecated alias for 'global'. Will be removed in v3.0.
-
     lambda_time_grid : list, optional
         Grid of time weight decay parameters. 0.0 = uniform weights (disabled).
         Must not contain inf. Default: [0, 0.1, 0.5, 1, 2, 5].
@@ -140,26 +136,9 @@ def __init__(
         seed: Optional[int] = None,
     ):
         # Validate method parameter
-        # 'local'/'global' are preferred; 'twostep'/'joint' are deprecated aliases
-        valid_methods = ("local", "twostep", "joint", "global")
+        valid_methods = ("local", "global")
         if method not in valid_methods:
             raise ValueError(f"method must be one of {valid_methods}, got '{method}'")
-        if method == "twostep":
-            warnings.warn(
-                "method='twostep' is deprecated and will be removed in v3.0. "
-                "Use method='local' instead.",
-                FutureWarning,
-                stacklevel=2,
-            )
-            method = "local"
-        if method == "joint":
-            warnings.warn(
-                "method='joint' is deprecated and will be removed in v3.0. "
-                "Use method='global' instead.",
-                FutureWarning,
-                stacklevel=2,
-            )
-            method = "global"
         self.method = method
 
         # Default grids from paper
@@ -913,22 +892,10 @@ def get_params(self) -> Dict[str, Any]:
     def set_params(self, **params) -> "TROP":
         """Set estimator parameters."""
         for key, value in params.items():
-            if key == "method" and value == "twostep":
-                warnings.warn(
-                    "method='twostep' is deprecated and will be removed in "
-                    "v3.0. Use method='local' instead.",
-                    FutureWarning,
-                    stacklevel=2,
-                )
-                value = "local"
-            if key == "method" and value == "joint":
-                warnings.warn(
-                    "method='joint' is deprecated and will be removed in "
-                    "v3.0. Use method='global' instead.",
-                    FutureWarning,
-                    stacklevel=2,
+            if key == "method" and value not in ("local", "global"):
+                raise ValueError(
+                    f"method must be one of ('local', 'global'), got '{value}'"
                 )
-                value = "global"
             if hasattr(self, key):
                 setattr(self, key, value)
             else:

diff --git a/docs/api/_autosummary/diff_diff.CallawaySantAnna.rst b/docs/api/_autosummary/diff_diff.CallawaySantAnna.rst
@@ -29,7 +29,7 @@
    .. autosummary::
 
       ~CallawaySantAnna.n_bootstrap
-      ~CallawaySantAnna.bootstrap_weight_type
+      ~CallawaySantAnna.bootstrap_weights
       ~CallawaySantAnna.alpha
       ~CallawaySantAnna.seed
       ~CallawaySantAnna.anticipation

diff --git a/docs/api/trop.rst b/docs/api/trop.rst
@@ -145,10 +145,6 @@ For the paper's full per-treated-cell estimator (Algorithm 2), use
 The global method is **faster** (single optimization vs N_treated optimizations).
 Treatment effects are **heterogeneous** per-observation residuals; ATT is their mean.
 
-``method='twostep'`` is a deprecated alias for ``method='local'`` and will be
-removed in v3.0. ``method='joint'`` is a deprecated alias for ``method='global'``
-and will be removed in v3.0.
-
 .. list-table::
    :header-rows: 1
    :widths: 20 40 40

diff --git a/docs/llms-full.txt b/docs/llms-full.txt
@@ -2,7 +2,7 @@
 
 > A Python library for Difference-in-Differences causal inference analysis. Provides sklearn-like estimators with statsmodels-style output for econometric analysis.
 
-- Version: 2.9.1
+- Version: 3.0.0
 - Repository: https://github.com/igerber/diff-diff
 - License: MIT
 - Dependencies: numpy, pandas, scipy (no statsmodels dependency)
@@ -589,7 +589,7 @@ Triply Robust Panel estimator (Athey, Imbens, Qu & Viviano 2025). Combines nucle
 
 ```python
 TROP(
-    method: str = "twostep",                  # "twostep" or "global" (or deprecated "joint")
+    method: str = "local",                     # "local" or "global"
     lambda_time_grid: list[float] = None,     # Time weight decay grid [0, 0.1, 0.5, 1, 2, 5]
     lambda_unit_grid: list[float] = None,     # Unit weight decay grid [0, 0.1, 0.5, 1, 2, 5]
     lambda_nn_grid: list[float] = None,       # Nuclear norm grid [0, 0.01, 0.1, 1, 10]
@@ -618,7 +618,7 @@ trop.fit(
 ```python
 from diff_diff import TROP
 
-trop = TROP(method='twostep', seed=42)
+trop = TROP(method='local', seed=42)
 results = trop.fit(data, outcome='outcome', treatment='treated',
                    unit='unit', time='period')
 results.print_summary()
@@ -1525,7 +1525,7 @@ clear_cache()
 
 ## Survey Support
 
-All estimators except `WooldridgeDiD` accept an optional `survey_design` parameter in `fit()`. Pass a `SurveyDesign` object to get design-based variance estimation. (WooldridgeDiD survey support is planned for Phase 10f.)
+All estimators accept an optional `survey_design` parameter in `fit()`. Pass a `SurveyDesign` object to get design-based variance estimation.
 
 ```python
 from diff_diff import SurveyDesign, CallawaySantAnna
@@ -1576,7 +1576,7 @@ sd_female, data_female = sd.subpopulation(data, mask=lambda df: df['sex'] == 'F'
 - Repeated cross-sections: `CallawaySantAnna(panel=False)`
 - Compatibility matrix: see `docs/choosing_estimator.rst` Survey Design Support section
 
-No R or Python package offers design-based variance estimation for modern heterogeneity-robust DiD estimators. (`WooldridgeDiD` does not yet accept `survey_design` — planned for Phase 10f.)
+No R or Python package offers design-based variance estimation for modern heterogeneity-robust DiD estimators.
 
 ## Linear Algebra Helpers