Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina GrĂ¼n, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference | Ho | Journal of Statistical Software
Authors: Daniel Ho, Kosuke Imai, Gary King, Elizabeth A. Stuart
Title: MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
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.

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Paper: MatchIt: Nonparametric Preprocessing for Parametric Causal Inference     Download PDF (Downloads: 9788)
MatchIt_2.4-18.tar.gz: R source package Download (Downloads: 1283; 500KB)
v42i08.R: R example code from the paper Download (Downloads: 1346; 7KB)

DOI: 10.18637/jss.v042.i08

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.