The Balance‐Sample Size Frontier in Matching Methods for Causal Inference

The Balance-Sample Size Frontier in Matching Methods for Causal Inference

We propose a simplified approach to matching for causal inference that simultaneously optimizes both balance (between the treated and control groups) and matched sample size. This procedure resolves two widespread tensions in the use of this popular methodology. First, current practice is to run a matching method that maximizes one balance metric (such as a propensity score or average Mahalanob...

Matching Methods for Causal Inference

Opiates for the Matches: Matching Methods for Causal Inference

In recent years, there has been a burst of innovative work on methods for estimating causal effects using observational data. Much of this work has extended and brought a renewed focus on old approaches such as matching, which is the focus of this review. The new developments highlight an old tension in the social sciences: a focus on research design versus a focus on quantitative models. This ...

Optimizing Balance and Sample Size in Matching Methods for Causal Inference

We propose a greatly simplified approach to matching for causal inference that simultaneously optimizes both balance (between the treated and control groups) and matched sample size. This procedure resolves two widespread (bias-variance trade off-related) tensions in the use of this powerful and popular methodology. First, current practice is to run a matching method that maximizes one balance ...

Comparative Effectiveness of Matching Methods for Causal Inference

Matching methods for causal inference selectively prune observations from the data in order to reduce model dependence. They are successful when simultaneously maximizing balance (between the treated and control groups on the pre-treatment covariates) and the number of observations remaining in the data set. However, existing matching methods either fix the matched sample size ex ante and attem...