Matching Methods for Causal Inference: A Review and a Look Forward

Matching methods for causal inference: A review and a look forward.

When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matchi...

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 ...

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...

Matching methods for causal inference: Designing observational studies

Much research in the social sciences attempts to estimate the effect of some intervention or “treatment” such as a school dropout prevention program or television watching. However, particularly in the social sciences, it is generally not possible to randomly assign units to receive the treatment condition or the control condition, and thus the resulting data are observational, where we simply ...