igerber · igerber · Jul 3, 2026 · Jul 3, 2026 · Jul 3, 2026 · Jul 3, 2026
diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt
@@ -2075,7 +2075,7 @@ clear_cache()
 
 ## Survey Support
 
-All estimators accept an optional `survey_design` parameter in `fit()`. Pass a `SurveyDesign` object to get design-based variance estimation.
+Most estimators accept an optional `survey_design` parameter in `fit()` (`SyntheticControl` rejects it as not yet supported); depth of support varies by estimator - see the compatibility matrix in `docs/choosing_estimator.rst` (Survey Design Support). Pass a `SurveyDesign` object to get design-based variance estimation.
 
 ```python
 from diff_diff import SurveyDesign, CallawaySantAnna
@@ -2120,7 +2120,7 @@ sd_female, data_female = sd.subpopulation(data, mask=lambda df: df['sex'] == 'F'
 
 **Key features:**
 - Taylor Series Linearization (TSL) variance with strata + PSU + FPC
-- Replicate weight variance: BRR, Fay's BRR, JK1, JKn, SDR (13 of 16 estimators, including dCDH)
+- Replicate weight variance: BRR, Fay's BRR, JK1, JKn, SDR (13 of 20 estimators, including dCDH)
 - Survey-aware bootstrap: multiplier at PSU (Hall-Mammen wild; dCDH, staggered) or Rao-Wu rescaled (SunAbraham, SyntheticDiD, TROP). SyntheticDiD bootstrap composes Rao-Wu rescaled per-draw weights with the weighted Frank-Wolfe variant of `_sc_weight_fw` (PR #355): each draw solves `min ||A·diag(rw)·ω - b||² + ζ²·Σ rw_i ω_i²` and composes `ω_eff = rw·ω/Σ(rw·ω)` for the SDID estimator. Pweight-only fits use constant `rw = w_control`; full designs use Rao-Wu. SDID's placebo (stratified permutation + weighted FW) and jackknife (PSU-level LOO with stratum aggregation, Rust & Rao 1996) paths also support pweight-only and full strata/PSU/FPC designs
 - DEFF diagnostics, subpopulation analysis, weight trimming (`trim_weights`)
 - Repeated cross-sections: `CallawaySantAnna(panel=False)`

diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt
@@ -2,7 +2,7 @@
 
 > A Python library for Difference-in-Differences (DiD) causal inference analysis. Provides sklearn-like estimators with statsmodels-style summary output for econometric analysis.
 
-diff-diff offers 19 estimators covering basic 2x2 DiD, modern staggered adoption methods, reversible (non-absorbing) treatments, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP.
+diff-diff offers 20 estimators covering basic 2x2 DiD, modern staggered adoption methods, reversible (non-absorbing) treatments, advanced panel estimators, nonlinear models, and diagnostic tools. It supports robust and cluster-robust standard errors, wild cluster bootstrap, formula and column-name interfaces, fixed effects (dummy and absorbed), complex survey designs (strata/PSU/FPC, replicate weights, design-based variance), and publication-ready output. The optional Rust backend accelerates compute-intensive estimators like Synthetic DiD and TROP.
 
 - Install: `pip install diff-diff`
 - License: MIT
@@ -104,7 +104,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")`
 
 ## Survey Support
 
-All estimators accept an optional `survey_design` parameter. Pass a `SurveyDesign` object to get design-based variance estimation:
+Most estimators accept an optional `survey_design` parameter (`SyntheticControl` does not yet support it); coverage and weight types vary by estimator - see the [Survey Design Support matrix](https://diff-diff.readthedocs.io/en/stable/choosing_estimator.html#survey-design-support). Pass a `SurveyDesign` object to get design-based variance estimation:
 
 - **Design elements**: strata, PSU, FPC, weight types (pweight/fweight/aweight), lonely PSU handling, nest
 - **Variance methods**: Taylor Series Linearization (TSL), replicate weights (BRR/Fay/JK1/JKn/SDR), survey-aware bootstrap

diff --git a/docs/choosing_estimator.rst b/docs/choosing_estimator.rst
@@ -771,9 +771,10 @@ If you're unsure which estimator to use:
 Survey Design Support
 ---------------------
 
-All estimators accept an optional ``survey_design`` parameter in ``fit()``.
+Most estimators support an optional ``survey_design`` parameter in ``fit()``
+(``SyntheticControl`` accepts the parameter but raises ``NotImplementedError``).
 Pass a :class:`~diff_diff.SurveyDesign` object to get design-based variance
-estimation. The depth of support varies by estimator:
+estimation. The depth of support varies by estimator and variance method:
 
 .. note::
 
@@ -820,7 +821,7 @@ estimation. The depth of support varies by estimator:
    * - ``ChaisemartinDHaultfoeuille``
      - pweight only
      - Full (TSL)
-     - --
+     - Full (analytical)
      - Group-level (warning)
    * - ``TripleDifference``
      - pweight only
@@ -869,9 +870,14 @@ estimation. The depth of support varies by estimator:
      - Multiplier at PSU
    * - ``SyntheticDiD``
      - pweight only
-     - Via bootstrap
+     - Full (method-specific)
      - --
      - Hybrid pairs-bootstrap + Rao-Wu rescaled (bootstrap only)
+   * - ``SyntheticControl``
+     - --
+     - --
+     - --
+     - --
    * - ``TROP``
      - pweight only
      - Via bootstrap
@@ -887,6 +893,11 @@ estimation. The depth of support varies by estimator:
      - Full (Binder TSL)
      - --
      - --
+   * - ``SpilloverDiD``
+     - pweight only
+     - Full (Binder TSL + Conley)
+     - --
+     - --
    * - ``BaconDecomposition``
      - Diagnostic
      - Diagnostic
@@ -897,24 +908,26 @@ estimation. The depth of support varies by estimator:
 
 - **Full**: All weight types (pweight/fweight/aweight) + strata/PSU/FPC + Taylor Series Linearization variance
 - **Full (pweight only)**: Full TSL with strata/PSU/FPC, but only ``pweight`` accepted (``fweight``/``aweight`` rejected because composition changes weight semantics)
-- **Via bootstrap**: Strata/PSU/FPC supported only with bootstrap variance. ``TROP`` uses bootstrap by default. ``SyntheticDiD`` supports strata/PSU/FPC on ``variance_method='bootstrap'`` via a hybrid pairs-bootstrap + Rao-Wu rescaling composition (see the ``Note (survey + bootstrap composition)`` in REGISTRY.md §SyntheticDiD); ``placebo`` and ``jackknife`` remain pweight-only.
+- **Via bootstrap**: Strata/PSU/FPC supported only with bootstrap variance (``TROP``, which uses bootstrap by default)
+- **Full (method-specific)**: ``SyntheticDiD`` supports strata/PSU/FPC on all three variance methods via method-specific survey paths — see the note below and the ``Note (survey support matrix)`` in REGISTRY.md §SyntheticDiD
 - **pweight only** (Weights column): Only ``pweight`` accepted; ``fweight``/``aweight`` raise an error
 - **Diagnostic**: Weighted descriptive statistics only (no inference)
 - **--**: Not supported
 
 .. note::
 
-   ``SyntheticDiD`` supports survey designs on ``variance_method='bootstrap'``
-   — both pweight-only and full strata/PSU/FPC — via a hybrid pairs-bootstrap
-   composed with per-draw Rao-Wu rescaled weights fed into a weighted
-   Frank-Wolfe re-estimation of ω and λ. See the
-   ``Note (survey + bootstrap composition)`` in REGISTRY.md §SyntheticDiD
-   for the objective form and argmin-set caveat.
-
-   ``variance_method='placebo'`` and ``variance_method='jackknife'`` remain
-   pweight-only — composing placebo permutations / leave-one-out with
-   Rao-Wu rescaling under the weighted objective is a separate derivation
-   (tracked in ``TODO.md``).
+   ``SyntheticDiD`` supports survey designs — both pweight-only and full
+   strata/PSU/FPC — on all three variance methods, each via a
+   method-specific path: ``bootstrap`` composes a hybrid pairs-bootstrap
+   with per-draw Rao-Wu rescaled weights fed into a weighted Frank-Wolfe
+   re-estimation of ω and λ; ``placebo`` switches to stratified
+   permutation (pseudo-treated draws within strata containing treated
+   units) with weighted-FW re-estimation, and FPC is a documented no-op
+   for the permutation test; ``jackknife`` switches to PSU-level
+   leave-one-out with stratum aggregation (Rust & Rao 1996).
+   Replicate-weight designs are rejected. See the
+   ``Note (survey support matrix)`` and the per-method composition notes
+   in REGISTRY.md §SyntheticDiD.
 
 For the full walkthrough with code examples, see the
 `survey tutorial <https://github.com/igerber/diff-diff/blob/main/docs/tutorials/16_survey_did.ipynb>`_.

diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md
@@ -4576,7 +4576,7 @@ variance from the distribution of replicate estimates.
   design structure is fixed and dropped replicates contribute zero to the
   sum without changing the scale. Survey df uses `n_valid - 1` for
   t-based inference.
-- **Note:** Replicate-weight support matrix (12 of 15 public estimators):
+- **Note:** Replicate-weight support matrix (13 of 20 public estimators):
   - **Supported**: CallawaySantAnna (reg/ipw/dr with or without covariates,
     no bootstrap; IF-based replicate variance is covariate-agnostic),
     ContinuousDiD (no bootstrap), EfficientDiD (no bootstrap),
@@ -4587,9 +4587,17 @@ variance from the distribution of replicate estimates.
     TwoWayFixedEffects (estimator-level refit with within-transformation),
     SunAbraham (estimator-level refit, replaces `vcov_cohort`),
     StackedDiD (estimator-level refit with Q-weight composition),
-    ImputationDiD (two-stage refit), TwoStageDiD (two-stage refit)
+    ImputationDiD (two-stage refit), TwoStageDiD (two-stage refit),
+    ChaisemartinDHaultfoeuille (closed-form cell-collapse replicate ATT,
+    multi-horizon and placebo paths; replicate + `n_bootstrap > 0` rejected
+    — see the ChaisemartinDHaultfoeuille Notes for the allocator contract)
   - **Rejected with NotImplementedError**: SyntheticDiD, TROP
-    (bootstrap-based variance), BaconDecomposition (diagnostic only)
+    (bootstrap-based variance), WooldridgeDiD, LPDiD, SpilloverDiD,
+    HeterogeneousAdoptionDiD (TSL-only survey paths; replicate designs
+    rejected at `fit()`), SyntheticControl (rejects `survey_design`
+    entirely)
+  - **BaconDecomposition** is diagnostic-only — outside the 20-estimator
+    count — and likewise rejects replicate designs
   - Estimators with replicate support reject replicate + bootstrap
     (replicate weights provide analytical variance)
 - **Note:** When invalid replicates are dropped in `compute_replicate_vcov`

diff --git a/docs/practitioner_decision_tree.rst b/docs/practitioner_decision_tree.rst
@@ -463,9 +463,10 @@ At a Glance
 What About the Other Estimators?
 --------------------------------
 
-diff-diff has 17 estimators covering advanced scenarios: Sun-Abraham for
+diff-diff has 20 estimators covering advanced scenarios: Sun-Abraham for
 interaction-weighted estimation, Imputation DiD and Two-Stage DiD for alternative
-staggered approaches, Stacked DiD, Efficient DiD, Triple Difference, TROP, and more.
+staggered approaches, Local Projections DiD, Stacked DiD, Efficient DiD,
+Triple Difference, TROP, and more.
 The six scenarios above cover the most common business use cases.
 
 For the full academic decision tree with all estimators, see :doc:`choosing_estimator`.

diff --git a/paper.bib b/paper.bib
@@ -220,7 +220,7 @@ @misc{Gerber2026
 
 @article{Abadie2010,
   author    = {Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens},
-  title     = {Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program},
+  title     = {Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of {California's} Tobacco Control Program},
   journal   = {Journal of the American Statistical Association},
   volume    = {105},
   number    = {490},
@@ -249,3 +249,32 @@ @misc{deChaisemartin2026
   primaryclass  = {econ.EM},
   doi       = {10.48550/arXiv.2405.04465}
 }
+
+@article{Dube2025,
+  author    = {Dube, Arindrajit and Girardi, Daniele and Jord{\`a}, {\`O}scar and Taylor, Alan M.},
+  title     = {A Local Projections Approach to Difference-in-Differences},
+  journal   = {Journal of Applied Econometrics},
+  volume    = {40},
+  number    = {5},
+  pages     = {741--758},
+  year      = {2025},
+  doi       = {10.1002/jae.70000}
+}
+
+@article{Binder1983,
+  author    = {Binder, David A.},
+  title     = {On the Variances of Asymptotically Normal Estimators from Complex Surveys},
+  journal   = {International Statistical Review},
+  volume    = {51},
+  number    = {3},
+  pages     = {279--292},
+  year      = {1983},
+  doi       = {10.2307/1402588}
+}
+
+@misc{pyfixest,
+  author    = {{The PyFixest Authors}},
+  title     = {{pyfixest}: Fast High-Dimensional Fixed Effect Estimation in {Python}},
+  year      = {2025},
+  url       = {https://github.com/py-econometrics/pyfixest}
+}