PPSCM — augsynth ``multisynth`` (Paglayan collective bargaining) ================================================================ .. currentmodule:: mlsynth Cross-validation against the reference implementation. ``PPSCM`` is mlsynth's port of partially-pooled SCM (Ben-Michael, Feller & Rothstein 2021), whose canonical implementation is ``augsynth::multisynth`` in R. This page reproduces the package's own `multisynth vignette `_ cell-for-cell — point estimates, the event study, **and** standard errors, the last cross-checked against a live R run of augsynth for *both* of its inference procedures. Data ---- The Paglayan (2018) public-sector collective-bargaining panel shipped at ``basedata/Teachingaugsynth.scv``: log per-pupil expenditure (``lnppexpend``) by ``State`` and ``year``, treatment ``cbr`` derived from ``YearCBrequired``. Restricted exactly as the vignette does — drop DC and WI, keep 1959–1997 — leaving **32 staggered-treated and 17 never-treated** states. Point estimates --------------- ================================ ========================= =========================== Quantity PPSCM (mlsynth) ``augsynth::multisynth`` ================================ ========================= =========================== Partial-pooling :math:`\nu` 0.2607 0.2607 Average ATT −0.011 −0.011 Global L2 imbalance 0.0026 0.003 :math:`\nu` (``time_cohort``) 0.3939 0.3939 Average ATT (``time_cohort``) −0.017 −0.018 Event-study path match to ``< 5e-4`` (reference) ================================ ========================= =========================== The OSQP solver (the same one augsynth uses) and the heuristic :math:`\nu` reproduce the reference to display precision; the per-horizon point estimates match to ``< 5e-4`` (unit cohorts) and ``< 2.2e-3`` (time cohorts). Inference — both of augsynth's procedures ----------------------------------------- augsynth offers two inference types; ``PPSCM`` reproduces **each**, method for method, and exposes them via ``inference_method``: * ``inference_method="jackknife"`` — the delete-one jackknife (``inf_type="jackknife"``). mlsynth's per-horizon SEs match augsynth's to ``< 1.5e-3``. * ``inference_method="bootstrap"`` — the Mammen wild/multiplier bootstrap (``inf_type="bootstrap"``), which is augsynth's **default** and the SE the vignette prints. The ported bootstrap reproduces the overall ATT SE (``0.022``) and the per-horizon path to ``< 4e-3`` (the residual is Monte-Carlo noise — R's RNG vs numpy's at ``n_boot``). ================================ ==================== ==================== Per-horizon SE (rel. time) jackknife bootstrap (default) ================================ ==================== ==================== augsynth 0.0186 … 0.0350 0.0225 … 0.0325 PPSCM 0.0185 … 0.0354 0.0224 … 0.0325 ================================ ==================== ==================== .. note:: The two procedures legitimately differ by ~10% (the bootstrap is wider early on). An earlier apparent "SE gap" was simply comparing mlsynth's *jackknife* to augsynth's *bootstrap* default — different methods. Matched method-for-method (verified against augsynth's R source and a live run), they agree. Reproduce --------- .. code-block:: bash python benchmarks/run_benchmarks.py ppscm_paglayan The durable case is ``benchmarks/cases/ppscm_paglayan.py`` (it cross-checks the point estimates, the event study, and both SE methods); the unit-level regressions are pinned in ``mlsynth/tests/test_ppscm.py`` (``test_matches_augsynth_vignette``, ``test_jackknife_se_matches_augsynth_vignette``, ``test_bootstrap_se_matches_augsynth_vignette``). All run on the in-repo data, so no R or network access is required.