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Estimation of multivalued treatment effects under conditional independence

Author

Listed:
  • Matias D. Cattaneo

    (University of Michigan - Ann Arbor)

  • David M. Drukker

    (StataCorp)

  • Ashley D. Holland

    (Cedarville University)

Abstract

This article discusses the poparms command, which implements two semiparametric estimators for multivalued treatment effects discussed in Cattaneo (2010, Journal of Econometrics 155: 138–154). The first is a properly reweighted inverse-probability weighted estimator, and the second is an efficient-influence function estimator, which can be interpreted as having the double-robust property. Our implementation jointly estimates means and quantiles of the potential outcome distributions, allowing for multiple, discrete treatment levels. These estimators are then used to estimate a variety of multivalued treatment effects. We discuss pre- and postestimation approaches that can be used in conjunction with our main implementation. We illustrate the program and provide a simulation study assessing the finite-sample performance of the inference procedures. Copyright 2013 by StataCorp LP.

Suggested Citation

  • Matias D. Cattaneo & David M. Drukker & Ashley D. Holland, 2013. "Estimation of multivalued treatment effects under conditional independence," Stata Journal, StataCorp LP, vol. 13(3), pages 407-450, September.
    • Handle: RePEc:tsj:stataj:v:13:y:2013:i:3:p:407-450
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