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%CEM: A SAS Macro to Perform Coarsened Exact Matching

Author

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  • Stefano VERZILLO
  • Paolo BERTA
  • Matteo BOSSI

Abstract

% CEM is a SAS macro which allows researchers to perform the recently introduced Coarsened Exact Matching (CEM) technique. CEM is a non-parametric matching method to avoid the confounding influence of pre-treatment control variables directly improving causal inference in quasi-experimental stud- ies. CEM authors originally provided few software solutions for R, Stata and SPSS packages to perform their matching algorithm. The % CEM macro integrates the already available software alternatives introducing a completely automated Coarsened Exact Matching macro for SAS users. Both the matching strategy -including some standard coarsening options- and the associated L1 multivariate imbalance measure are provided. An empirical application estimating the causal effect of regional health systems on the intra-hospital mortality using multiple artificial datasets from a large administrative database completes the paper.

Suggested Citation

  • Stefano VERZILLO & Paolo BERTA & Matteo BOSSI, 2015. "%CEM: A SAS Macro to Perform Coarsened Exact Matching," Departmental Working Papers 2015-22, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    • Handle: RePEc:mil:wpdepa:2015-22
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    File URL: http://wp.demm.unimi.it/files/wp/2015/DEMM-2015_22wp.pdf
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    References listed on IDEAS

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    1. Camillo, Furio & D'Attoma, Ida, 2012. "%GI: A SAS Macro for Measuring and Testing Global Imbalance of Covariates within Subgroups," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(c01).
    2. 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.
    3. Iacus, Stefano & King, Gary & Porro, Giuseppe, 2009. "cem: Software for Coarsened Exact Matching," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 30(i09).
    4. Iacus, Stefano M. & King, Gary & Porro, Giuseppe, 2011. "Multivariate Matching Methods That Are Monotonic Imbalance Bounding," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 345-361.
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