MatchIt: Nonparametric preprocessing for parametric causal inference

Daniel E. Ho, Kosuke Imai, Gary King, Elizabeth A. Stuart

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1-28
Number of pages28
JournalJournal of Statistical Software
Volume42
Issue number8
DOIs
StatePublished - Jun 2011

Keywords

  • Balance
  • Causal inference
  • Matching methods
  • Preprocessing
  • R

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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