coresynth - Fast and Unified Synthetic Control Methods
A 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>.
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causal-inferencepanel-datasynthetic-controlsynthetic-control-methodsynthetic-difference-in-differencesopenblascppopenmp
5.94 score 4 stars 9 scripts 1.2k downloadsfixes - Event Studies and Inference for Difference-in-Differences
Provides tools for event-study analysis and inference in difference-in-differences (DiD) designs with panel data, from the classic two-way fixed-effects (TWFE) event study to estimators for staggered adoption. Includes event_study() for event-study curves (dynamic effects by relative time), att() for aggregated ATT estimation (overall, by cohort, by calendar time), did() for basic two-way fixed-effects DiD, and honest_sensitivity() for sensitivity analysis under violations of parallel trends (Rambachan and Roth, 2023), all running on an internal C++ fixed-effects OLS engine. Supports multiple modern estimators: Callaway-Sant'Anna (2021), Sun-Abraham (2021), Borusyak-Jaravel-Spiess (2024), Wooldridge TWM, and Deb et al. FLEX. Results plot via base plot() methods.
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causal-inferencedifference-in-differenceseconometricspanel-dataopenblascppopenmp
5.89 score 56 scripts 430 downloads