@article{JSSv042i08,
title={MatchIt: Nonparametric Preprocessing for Parametric Causal Inference},
volume={42},
url={https://www.jstatsoft.org/index.php/jss/article/view/v042i08},
doi={10.18637/jss.v042.i08},
abstract={<b>MatchIt</b> implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. <b>MatchIt</b> 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 <b>MatchIt</b>, researchers can use whatever parametric model they would have used without <b>MatchIt</b>, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. <b>MatchIt</b> is an R program, and also works seamlessly with <b>Zelig</b>.},
number={8},
journal={Journal of Statistical Software},
author={Ho, Daniel and Imai, Kosuke and King, Gary and Stuart, Elizabeth A.},
year={2011},
pages={1–28}
}