Synthetic estimation for the complier average causal effect

Abstract

We propose an improved estimator of the complier average causal effect (CACE). Researchers typically choose a presumably consistent estimator for CACE in studies with noncompliance when many other lower variance estimators may be available. We propose a synthetic estimator that combines information across all available estimators, leveraging the efficiency in lower variance estimators while maintaining low bias. Our approach minimizes an estimate of the mean squared error of all convex combinations of the candidate estimators. We derive the asymptotic distribution of the synthetic estimator and demonstrate its good performance in simulation, displaying robustness to inclusion of even high‐bias estimators.