- **SDID `variance_method="bootstrap"` survey support restored** via a hybrid pairs-bootstrap + Rao-Wu rescaling composed with a weighted Frank-Wolfe kernel. Each bootstrap draw first performs the unit-level pairs-bootstrap resampling specified by Arkhangelsky et al. (2021) Algorithm 2 (`boot_idx = rng.choice(n_total)`), and *then* applies Rao-Wu rescaled per-unit weights (Rao & Wu 1988) sliced over the resampled units — NOT a standalone Rao-Wu bootstrap. New Rust kernel `sc_weight_fw_weighted` (and `_with_convergence` sibling) accepts a per-coordinate `reg_weights` argument so the FW objective becomes `min ||A·ω - b||² + ζ²·Σ_j reg_w[j]·ω[j]²`. New Python helpers `compute_sdid_unit_weights_survey` and `compute_time_weights_survey` thread per-control survey weights through the two-pass sparsify-refit dispatcher (column-scaling Y by `rw` for the loss, `reg_weights=rw` for the penalty on the unit-weights side; weighted column-centering + row-scaling Y by `sqrt(rw)` for the loss with uniform reg on the time-weights side). `_bootstrap_se` survey branch composes the per-draw `rw` (Rao-Wu rescaling for full designs, constant `w_control` for pweight-only fits) with the weighted-FW helpers, then composes `ω_eff = rw·ω/Σ(rw·ω)` for the SDID estimator. Coverage MC artifact extended with a `stratified_survey` DGP (BRFSS-style: N=40, strata=2, PSU=2/stratum); the bootstrap row's near-nominal calibration is the validation gate (target rejection ∈ [0.02, 0.10] at α=0.05). New regression tests across `test_methodology_sdid.py::TestBootstrapSE` (single-PSU short-circuit, full-design and pweight-only succeeds-tests, zero-treated-mass retry, deterministic Rao-Wu × boot_idx slice) and `test_survey_phase5.py::TestSyntheticDiDSurvey` (full-design ↔ pweight-only SE differs assertion). See REGISTRY.md §SyntheticDiD ``Note (survey + bootstrap composition)`` for the full objective and the argmin-set caveat.
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