MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

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MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

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Title: MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
Author: King, Gary ORCID  0000-0002-5327-7631 ; Ho, Daniel; Stuart, Elizabeth A.; Imai, Kosuke

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Citation: Stuart, Elizabeth A., Gary King, Kosuke Imai, and Daniel Ho. 2011. MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software 42, no. 8.
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Abstract: MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.
Published Version: http://www.jstatsoft.org/v42/i08
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11130519
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