MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

Authors: Daniel Ho, Kosuke Imai, Gary King, Elizabeth Stuart

"At MatchIt, we don't make parametric models, we make parametric models work better."
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.

We're pleased to report that the article on which MatchIt is based won the Warren Miller Prize for the best paper in Political Analysis that year and, separately, has been named a Fast Breaking Paper by Thomson Reuters' ScienceWatch, for being the article with the largest percentage increase in citations among those in the top 1% of total citations across the social sciences in the last two years. (You may be interested in this interview: HTML | PDF ).

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