Package: SCMs 0.1.1

SCMs: Specification Curve Analysis for Synthetic Control Methods

Estimates synthetic control models while exposing the implementation choices that define the synthetic counterfactual: how matching variables are constructed, how predictor weights are set, which constraints the donor weights satisfy (simplex, lasso, ridge, penalized), whether an intercept is included, and which outcome model corrects for bias (OLS, ridge, lasso, augmented synthetic control). Runs specification-curve analysis over a grid of these choices (Simonsohn, Simmons and Nelson (2020) <doi:10.1038/s41562-020-0912-z>), with placebo-in-space and bootstrap inference for single specifications and placebo inference for the curve as a whole, and uses 'xgboost' models with SHAP values (Lundberg and Lee (2017) <doi:10.48550/arXiv.1705.07874>) to show which choices drive the estimates. Methods follow Abadie, Diamond and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746>, Abadie (2021) <doi:10.1257/jel.20191450>, Ben-Michael, Feller and Rothstein (2021) <doi:10.1080/01621459.2021.1929245> and Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>.

Authors:Zubin Jelveh [aut, cre]