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Coarsened Exact Matching (CEM) is a Monotonoic Imbalance Bounding (MIB) matching method -- which means that the balance between the treated and control groups is chosen by the user ex ante rather than discovered through the usual laborious process of checking after the fact and repeatedly reestimating, and so that adjusting the imbalance on one variable has no effect on the maximum imbalance of any other. CEM also strictly bounds through ex ante user choice both the degree of model dependence and the average treatment effect estimation error, eliminates the need for a separate procedure to restrict data to common empirical support, meets the congruence principle, is robust to measurement error, works well with multiple imputation methods for missing data, and is extremely fast computationally even with very large data sets. CEM also works well for multicategory treatments, determining blocks in experimental designs, and evaluating extreme counterfactuals (, ).
CEM can be performed with MATCHIT by setting method = "cem", which automatically loads the cem package. The following examples of CEM (with automatic coarsening) can also be run by typing demo(cem):
m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree
+ re74 + re75, data = lalonde, method = "cem")