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

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          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.

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          Most cited references 63

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          Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review

           Guido Imbens (2004)
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            Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

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              Characterizing Selection Bias Using Experimental Data

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                Author and article information

                Journal
                2010-10-27
                Article
                10.1214/09-STS313
                1010.5586

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                IMS-STS-STS313
                Statistical Science 2010, Vol. 25, No. 1, 1-21
                Published in at http://dx.doi.org/10.1214/09-STS313 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
                stat.ME
                vtex

                Methodology

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