Estimation of Multivalued Treatment Effects under Conditional Independence

Author:

Cattaneo Matias D.1,Drukker David M.2,Holland Ashley D.3

Affiliation:

1. Department of Economics University of Michigan Ann Arbor, MI

2. StataCorp College Station, TX

3. 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.

Publisher

SAGE Publications

Subject

Mathematics (miscellaneous)

Reference25 articles.

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4. ChenX., HongH., and TarozziA. 2004. Semiparametric efficiency in GMM models of nonclassical measurement errors, missing data and treatment effects. Discussion Paper No. 1644, Cowles Foundation. http://cowles.econ.yale.edu/P/cd/d16a/d1644.pdf.

5. Semiparametric efficiency in GMM models with auxiliary data

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