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

TL;DR

This paper reviews the development and current state of matching methods for causal inference from observational data, providing a unified framework and guidance for future research across multiple disciplines.

Contribution

It offers a comprehensive synthesis of matching methods literature, clarifies their use, and suggests directions for future research in causal inference.

Findings

Matching methods help reduce bias in observational studies.

The literature on matching is scattered across disciplines.

Guidance is provided for selecting and developing matching techniques.

Abstract

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 1970s, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods---or developing methods related to matching---do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.