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CEM: Coarsened Exact Matching in Stata


  • Matthew Blackwell

    () (Harvard University)

  • Stefano Iacus

    (Universita degli Studi di Milano, Italy)

  • Gary King

    (Harvard University)

  • Giuseppe Porro

    (Universita degli Studi di Trieste, Italy)


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.

Suggested Citation

  • Matthew Blackwell & Stefano Iacus & Gary King & Giuseppe Porro, 2010. "CEM: Coarsened Exact Matching in Stata," BOS10 Stata Conference 8, Stata Users Group.
  • Handle: RePEc:boc:bost10:8

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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," Review of Economic Studies, Oxford University Press, vol. 64(4), pages 605-654.
    4. Giuseppe Porro & Stefano Maria Iacus, 2009. "Random Recursive Partitioning: a matching method for the estimation of the average treatment effect," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 163-185.
    5. Stefano Iacus & Gary King & Giuseppe Porro, 2008. "Matching for Causal Inference Without Balance Checking," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1073, Universitá degli Studi di Milano.
    6. repec:cup:apsrev:v:95:y:2001:i:01:p:49-69_00 is not listed on IDEAS
    7. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502.
    8. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(03), pages 199-236, June.
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