cem: Coarsened Exact Matching in Stata

Abstract

This paper introduces a Stata implementation of Coarsened Exact Matching (CEM), a new method for improving the estimation of causal effects by reducing imbalance in co-variates between treated and control groups. CEM is faster, easier to use and understand, requires fewer assumptions, more easily automated, and possesses more attractive statistical properties for many applications than existing matching methods. In CEM, users temporarily coarsen their data, exact match on these coarsened data, then run their analysis on the uncoarsened, matched data. CEM bounds the degree of model dependence and causal effect estimation error by ex ante user choice, is montonic imbalance bounding (so that reducing the maximum imbalance on one variable has no e ect 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 data sets. Other matching methods inheret [sic] many of CEM's properties when applied to further match data preprocessed by CEM. The library cem implements the CEM algorithm in Stata.