cem: Coarsened exact matching in Stata

Matthew Blackwell (), Stefano Iacus (), Gary King and Giuseppe Porro
Additional contact information
Matthew Blackwell: Harvard University

Stata Journal, 2009, vol. 9, issue 4, 524-546

Abstract: In this article, we introduce a Stata implementation of coarsened exact matching, a new method for improving the estimation of causal effects by reducing imbalance in covariates between treated and control groups. Coarsened exact matching is faster, is easier to use and understand, requires fewer assumptions, is more easily automated, and possesses more attractive statistical properties for many applications than do existing matching methods. In coarsened exact matching, users temporarily coarsen their data, exact match on these coarsened data, and then run their analysis on the uncoarsened, matched data. Coarsened exact matching bounds the degree of model dependence and causal effect estimation error by ex ante user choice, is monotonic imbalance bounding (so that reducing the maximum imbalance on one variable has no effect on others), does not require a separate procedure to restrict data to common support, meets the congruence principle, is approximately invariant to measurement error, balances all nonlinearities and interactions in sample (i.e., not merely in expectation), and works with multiply imputed datasets. Other matching methods inherit many of the coarsened exact matching method’s properties when applied to further match data preprocessed by coarsened exact matching. The cem command implements the coarsened exact matching algorithm in Stata. Copyright 2009 by StataCorp LP.

Keywords: cem; imbalance; matching; coarsened exact matching; causal inference; balance; multiple imputation (search for similar items in EconPapers)
Date: 2009
Note: Due to licensing issues, the software for this article cannot be made available from the Stata Journal web site.
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Citations: View citations in EconPapers (457)

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Working Paper: CEM: Coarsened Exact Matching in Stata (2010)
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