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Causal Inference without Balance Checking: Coarsened Exact Matching

  • Stefano M. Iacus (a1), Gary King (a2) and Giuseppe Porro (a3)
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.

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e-mail: king@harvard.edu (corresponding author)
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Alberto Abadie , and Javier Gardeazabal . 2003. The economic costs of conflict: A case study of the Basque Country. American Economic Review 93: 113–32.

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Stefano M. Iacus , Gary King , and Giuseppe Porro . 2009. CEM: Coarsened Exact Matching Software. Journal of Statistical Software 30(9), http://gking.harvard.edu/cem.

Stefano M. Iacus , Gary King , and Giuseppe Porro . 2011. Multivariate matching methods that are Monotonic Imbalance Bounding. Journal of the American Statistical Association. http://gking.harvard.edu/files/abs/cem-math-abs.shtml.

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Donald B. Rubin 1987. Multiple imputation for nonresponse in surveys. New York: John Wiley.

Donald B. Rubin 2006. Matched sampling for causal effects. Cambridge, UK: Cambridge University Press.

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Political Analysis
  • ISSN: 1047-1987
  • EISSN: 1476-4989
  • URL: /core/journals/political-analysis
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