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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 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.Comment: 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
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