Correlated Synthetic Controls

Abstract

Synthetic Control methods have recently gained considerable attention in applications with only one treated unit. Their popularity is partly based on the key insight that we can predict good synthetic counterfactuals for our treated unit. However, this insight of predicting counterfactuals is generalisable to microeconometric settings where we often observe many treated units. We propose the Correlated Synthetic Controls (CSC) estimator for such situations: intuitively, it creates synthetic controls that are correlated across individuals with similar observables. When treatment assignment is correlated with unobservables, we show that the CSC estimator has more desirable theoretical properties than the difference-in-differences estimator. We also utilise CSC in practice to obtain heterogeneous treatment effects in the well-known Mariel Boatlift study, leveraging additional information from the PSID.