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MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

  • Daniel Ho
  • Kosuke Imai
  • Gary King
  • Elizabeth A. Stuart

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.

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Article provided by American Statistical Association in its journal Journal of Statistical Software.

Volume (Year): 42 ()
Issue (Month): i08 ()
Pages:

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Handle: RePEc:jss:jstsof:42:i08
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  1. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502.
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