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The Stata Journal
Volume 9 Number 4: pp. 524-546



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cem: Coarsened exact matching in Stata

Matthew Blackwell
Harvard University
Cambridge, smrm
MA
mblackwell@iq.harvard.edu
Stefano Iacus
Universitá degli Studi di Milano
Milano,
Italy
stefano.iacus@unimi.it
Gary King
Harvard University
Cambridge, MA
king@harvard.edu
Giuseppe Porro
Universitá degli Studi di Trieste
Trieste,
Italy
giuseppe.porro@econ.units.it
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.
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View all articles with these keywords: cem, imbalance, matching, coarsened exact matching, causal inference, balance, multiple imputation

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