Estimation of multivalued treatment effects under conditional independence
Matias D. Cattaneo
Department of Economics
University of Michigan
Ann Arbor, MI
[email protected]
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David M. Drukker
StataCorp
College Station, TX
[email protected]
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Ashley D. Holland
Department of Science and Mathematics
Cedarville University
Cedarville, OH
[email protected]
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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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