Authors
Daniel E Ho, Kosuke Imai, Gary King, Elizabeth Stuart
Publication date
2011
Journal
Journal of Statistical Software
Volume
42
Issue
8
Pages
1-28
Description
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.
Total citations
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Scholar articles
EA Stuart, G King, K Imai, D Ho - Journal of statistical software, 2011
D Ho, K Imai, G King, E Stuart, A Whitworth - Version.[Google Scholar], 2018
DE Ho, K Imai, G King, EA Stuart - Version 0.8. Used with permission, 2004
D Ho, K Imai, G King, E Stuart - Nonparametric preprocessing for parametric causal …, 2004
D Ho, K Imai, G King, E Stuart - Harvard Institute for Quantitative Social Sciences, 2018
D Ho, I Kosuke, G King, E Stuart - Retrived from http://gking. harvard. edu/matchit, 2013
DE Ho, K Imai, G King, EA Stuart - Journal of Statistical Software. http://gking. harvard. edu …
D Ho, K Imai, G King, E Stuart - Avail able at http://gking. harvard. edu/matchit …, 2007