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COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS

Published online by Cambridge University Press:  06 November 2018

Craig A. Rolling ,
Yuhong Yang  and
Dagmar Velez
Craig A. Rolling*
Affiliation:
Saint Louis University
Yuhong Yang
Affiliation:
University of Minnesota
Dagmar Velez
Affiliation:
Saint Louis University
*
*Address correspondence to Craig A. Rolling, Department of Epidemiology and Biostatistics, Saint Louis University, St. Louis, Missouri, USA; e-mail: [email protected].
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Abstract

Estimating a treatment’s effect on an outcome conditional on covariates is a primary goal of many empirical investigations. Accurate estimation of the treatment effect given covariates can enable the optimal treatment to be applied to each unit or guide the deployment of limited treatment resources for maximum program benefit. Applications of conditional treatment effect estimation are found in direct marketing, economic policy, and personalized medicine. When estimating conditional treatment effects, the typical practice is to select a statistical model or procedure based on sample data. However, combining estimates from the candidate procedures often provides a more accurate estimate than the selection of a single procedure. This article proposes a method of model combination that targets accurate estimation of the treatment effect conditional on covariates. We provide a risk bound for the resulting estimator under squared error loss and illustrate the method using data from a labor skills training program.

Type
ARTICLES
Information
Econometric Theory , Volume 35 , Issue 6 , December 2019 , pp. 1089 - 1110
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

The authors would like to thank Arthur Lewbel and four anonymous referees for their helpful comments and suggestions that have improved the article.

References

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