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Causal Inference without Balance Checking: Coarsened Exact Matching

Published online by Cambridge University Press:  04 January 2017

Stefano M. Iacus ,
Gary King  and
Giuseppe Porro
Stefano M. Iacus
Affiliation:
Department of Economics, Business and Statistics, University of Milan, Via Conservatorio 7, I-20124 Milan, Italy. e-mail: stefano.iacus@unimi.it
Gary King*
Affiliation:
Institute for Quantitative Social Science, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138
Giuseppe Porro
Affiliation:
Department of Economics and Statistics, University of Trieste, P.le Europa 1, I-34127 Trieste, Italy. e-mail: giuseppe.porro@econ.units.it
*
e-mail: king@harvard.edu (corresponding author)
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Abstract

We discuss a method for improving causal inferences called “Coarsened Exact Matching” (CEM), and the new “Monotonic Imbalance Bounding” (MIB) class of matching methods from which CEM is derived. We summarize what is known about CEM and MIB, derive and illustrate several new desirable statistical properties of CEM, and then propose a variety of useful extensions. We show that CEM possesses a wide range of statistical properties not available in most other matching methods but is at the same time exceptionally easy to comprehend and use. We focus on the connection between theoretical properties and practical applications. We also make available easy-to-use open source software for R, Stata, and SPSS that implement all our suggestions.

Information

Type
Research Article
Information
Political Analysis , Volume 20 , Issue 1 , Winter 2012 , pp. 1 - 24
Copyright
Copyright © The Author 2011. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Edited by Jonathan N. Katz

Authors' note: Open source R, Stata, and SPSS software to implement the methods described herein (called CEM) is available at http://gking.harvard.edu/cem; the CEM algorithm is also available via a standard interface offered in the R package MatchIt. Thanks to Erich Battistin, Nathaniel Beck, Matt Blackwell, Andy Eggers, Adam Glynn, Justin Grimmer, Jens Hainmueller, Ben Hansen, Kosuke Imai, Guido Imbens, Fabrizia Mealli, Walter Mebane, Clayton Nall, Enrico Rettore, Jamie Robins, Don Rubin, Jas Sekhon, Jeff Smith, Kevin Quinn, and Chris Winship for helpful comments. All information necessary to replicate the results in this paper appear in Iacus, King, and Porro (2011b).

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