The Trouble with Coarsened Exact Matching
37 Pages Posted: 7 Oct 2020
Date Written: October 6, 2020
“Balancing” methods, using matching or reweighting to improve the balance between treated and control units, have become central methodological tools for causal inference in the social sciences using cross-sectional observational data. We address here one method which has attained substantial popularity in political science, Coarsened Exact Matching (CEM) (Iacus, King, and Porro 2012). We report evidence that CEM performs substantially worse than an array of other methods and explain why it does so. We replicate five recent papers that use CEM and compare CEM-based results to those from other balancing methods. CEM drops substantially more observations, does so in non-obvious ways, can severely misidentify average treatment effects, and is much less precise than other methods. Our advice: never use CEM as the sole balancing method, and there is little to be said for using it at all.
The Online Appendix for this paper is available at http://ssrn.com/abstract=3705007.
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