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. 2010 Feb 1;25(1):1-21.
doi: 10.1214/09-STS313.

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

Elizabeth A Stuart  1
Affiliations

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

Elizabeth A Stuart. Stat Sci. .

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 1970's, 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.

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Figures

Figure 1
Figure 1
Matches chosen using 1:1 nearest neighbor matching on propensity score. Black dots indicate matched individuals; grey unmatched individuals. Data from Stuart and Green (2008).
Figure 2
Figure 2
Plot of standardized difference of means of 10 covariates before and after matching. Data from Stuart and Green (2008).
See this image and copyright information in PMC

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