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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
cattaneo@umich.edu
David M. Drukker
StataCorp
College Station, TX
ddrukker@stata.com
Ashley D. Holland
Department of Science and Mathematics
Cedarville University
Cedarville, OH
aholland@cedarville.edu
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.

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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