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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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          Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review

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            Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

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              Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study.

              Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. 2004 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                2010-10-27
                Article
                10.1214/09-STS313
                1010.5586
                7d69f528-3682-4a05-b62c-0fb7dfc1d118

                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
                Methodology

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