coresynth: Fast and Unified Synthetic Control Methods
Source:R/coresynth-package.R
coresynth-package.RdA unified 'Formula' interface to the Synthetic Control Method (SCM) and related panel-data causal inference estimators: Synthetic Difference-in-Differences (SDID), Generalized Synthetic Control (GSC), Matrix Completion (MC), Time-Aware Synthetic Control (TASC), and Synthetic Interventions (SI), together with an experimental-design variant. Computational bottlenecks (quadratic programming, singular value decomposition, and Kalman filtering) are implemented in 'C++' via 'RcppArmadillo'. Methods are described in Abadie, Diamond and Hainmueller (2010) doi:10.1198/jasa.2009.ap08746 , Arkhangelsky, Athey, Hirshberg, Imbens and Wager (2021) doi:10.1257/aer.20190159 , Xu (2017) doi:10.1017/pan.2016.2 , Athey, Bayati, Doudchenko, Imbens and Khosravi (2021) doi:10.1080/01621459.2021.1891924 , and Agarwal, Shah and Shen (2025) doi:10.1287/opre.2025.1590 .
Parallel computation
The in-space placebo refits of mspe_ratio_pval() and sdid_inference(),
and the multi-start search of scm_fit(), run in parallel with OpenMP.
Set options(coresynth.threads = n) to cap the number of threads. Left
unset, the OpenMP default applies (all cores, or OMP_NUM_THREADS /
OMP_THREAD_LIMIT when set), except under R CMD check --as-cran, where at
most two threads are used. The results do not depend on the thread count.