Combining Matching and Synthetic Control to Trade off Biases from Extrapolation and Interpolation

ABSTRACT: The synthetic control (SC) method is widely used in comparative case studies to adjust for differences in pre-treatment characteristics. SC limits extrapolation bias at the potential expense of interpolation bias, whereas traditional matching estimators have the opposite properties. This complementarity motives us to propose a matching and synthetic control (or MASC) estimator as a model averaging estimator that combines the standard SC and matching estimators. We show how to use a rolling-origin cross-validation procedure to train the MASC to resolve trade-offs between interpolation and extrapolation bias. We use a series of empirically-based placebo and Monte Carlo simulations to shed light on when the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we

SUBMITTER: Kellogg M 

PROVIDER: S-EPMC9197080 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature