cem: Coarsened exact matching in Stata
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.
View all articles by these authors:
Matthew Blackwell, Stefano Iacus, Gary King, Giuseppe Porro
View all articles with these keywords:
cem, imbalance, matching, coarsened exact matching, causal inference, balance, multiple imputation
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