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The Stata Journal
Volume 13 Number 3: pp. 407-450

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Estimation of multivalued treatment effects under conditional independence

Matias D. Cattaneo
Department of Economics
University of Michigan
Ann Arbor, MI
David M. Drukker
College Station, TX
Ashley D. Holland
Department of Science and Mathematics
Cedarville University
Cedarville, OH
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.
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View all articles by these authors: Matias D. Cattaneo, David M. Drukker, Ashley D. Holland

View all articles with these keywords: poparms, bfit, inverse-probability weighting, treatment effects, semiparametric estimation, unconfoundedness, generalized propensity score, multivalued treatment effects

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