Estimation of multivalued treatment effects under conditional independence

Matias Cattaneo, David Drukker and Ashley D. Holland ()
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Ashley D. Holland: Cedarville University

Stata Journal, 2013, vol. 13, issue 3, 407-450

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.

Keywords: poparms; bfit; inverse-probability weighting; treatment effects; semiparametric estimation; unconfoundedness; generalized propensity score; multivalued treatment effects (search for similar items in EconPapers)
Date: 2013
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