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Changelog

Source: NEWS.md

coresynth 0.2.1

Bug fixes

  • 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".

New features

  • 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.

coresynth 0.2.0

New features

  • Conformal inference (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().

Minor improvements

  • 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.

coresynth 0.1.0

First public release.

Methods

  • SCM (Abadie, Diamond & Hainmueller 2010): Synthetic Control Method with unified formula interface. Supports predictor variables via 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().
  • SDID (Arkhangelsky et al. 2021): Synthetic Difference-in-Differences. Supports time-varying covariates (covariates =), sharp and staggered adoption. Inference: sdid_inference() with placebo / bootstrap / jackknife / jackknife_global.
  • GSC (Xu 2017): Generalised Synthetic Control with interactive fixed effects. Supports time-varying covariates via the full EM algorithm, sharp and staggered adoption. Inference: parametric bootstrap (gsc_boot()) and non-parametric (gsc_inference()).
  • MC (Athey et al. 2021): Matrix Completion via nuclear-norm regularisation (Soft-Impute). Supports sharp and staggered adoption.
  • TASC (Rho et al. 2026): Time-Aware Synthetic Control via Kalman EM. Supports sharp and staggered adoption.
  • SI (Agarwal et al. 2025): Synthetic Interventions via SI-PCR. Supports sharp, staggered, multi-arm (K > 1), and staggered × multi-arm. Inference: si_inference() with bootstrap / jackknife / jackknife_global.
  • SCM-Design (Abadie & Zhao 2026): scm_design() with base / weakly_targeted / unit_level variants, blank-period permutation test, and split-conformal confidence intervals.

Unified API

  • Single 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.

Performance

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.