v_selection = "oos" (outcomes-only case) previously fit candidate W(V) on
the full pre-treatment outcome matrix and restricted only the MSPE evaluation
to the validation window, allowing the V optimiser to fit the validation
period indirectly (a data leak relative to Abadie (2021) S.3.2). The new
.scm_oos_outcomes() implements the correct train/validation split:
candidate W(V) are fitted on training-half outcomes only, V* minimises
validation-half MSPE, and W* is refit with V* on the outcomes of the last
floor(T_pre/2) pre-treatment periods. For OOS fits, v_weights now has
floor(T_pre/2) entries and a new v_rows field records which periods they
refer to. This changes numerical results for v_selection = "oos".scale_predictors (default TRUE): predictor rows supplied via
predictors = are now divided by their standard deviation across all units
before optimisation, matching the Synth reference implementation (Abadie,
Diamond & Hainmueller 2011, JSS). predictor_table continues to report
values on the original scale. This changes numerical results for SCM fits
with user-supplied predictors unless scale_predictors = FALSE.placebo_in_time(): in-time placebo (backdating) test for sharp SCM fits
(Abadie, Diamond & Hainmueller 2015; Abadie & Vives-i-Bastida 2022).loo_donors(): leave-one-out donor robustness check with the predictor
weights V held fixed (Abadie, Diamond & Hainmueller 2015, footnote 20).build_predictor_matrices() now errors with an informative message if a
pred() time window produces missing or non-finite predictor values.conformal_inference()): permutation-based p-values and
confidence intervals following Chernozhukov, Wüthrich & Zhu (2021).
Works with sharp fits across all supported estimation methods
(scm, sdid, gsc, mc, si). The counterfactual proxy is re-estimated
under the null on all T periods (essential for finite-sample validity per
CWZ S.2.2), and p-values are obtained via moving-block (cyclic-shift) permutation
of the estimated residuals. Confidence intervals are constructed by test
inversion over a user-supplied or automatically chosen grid.
Returns a coresynth_inference subclass compatible with tidy() and glance().panel_to_matrices(): fill loop replaced by vectorised match() + matrix-index
assignment; removes an O(n × (T + N)) bottleneck in the shared data-prep path.tasc.cpp: safe_inv_sympd() helper added so the Kalman filter degrades to
pinv instead of aborting when the innovation covariance is not numerically PD.%||% null-coalescing helper centralised in utils.R; duplicate definitions in
broom.R and plot.R removed.check_sharp_adoption() (unused internal function) removed.First public release.
pred(), out-of-sample V selection (v_selection = "oos"),
donor filtering (donor_mspe_threshold), penalised SCM (lambda_pen), and staggered adoption.
Inference: MSPE ratio permutation test via mspe_ratio_pval().covariates =), sharp and staggered adoption.
Inference: sdid_inference() with placebo / bootstrap / jackknife / jackknife_global.gsc_boot()) and non-parametric (gsc_inference()).si_inference() with bootstrap / jackknife / jackknife_global.scm_design() with base / weakly_targeted / unit_level variants,
blank-period permutation test, and split-conformal confidence intervals.scm_fit(outcome ~ treatment | unit + time, data, method = ...) entry point for all methods.panel_to_tensor() for multi-arm SI data preparation.broom integration: tidy(), glance(), augment() for all methods and inference objects.plot.coresynth(): trend, gap, and weights plots via ggplot2.export_json(): JSON export for reproducibility.All core optimisations implemented in C++ via RcppArmadillo:
50–70x faster than the Synth package for typical panel sizes (N_co ≤ 30).
src/inference.cpp placebo loops parallelised with OpenMP.