Electronic Journal of Statistics

Estimating multiple treatment effects using two-phase semiparametric regression estimators

Cindy Yu, Jason Legg, and Bin Liu

Full-text: Open access

Abstract

We propose a semiparametric two-phase regression estimator with a semiparametric generalized propensity score estimator for estimating average treatment effects in the presence of the first-phase sampling. The proposed estimator can be easily extended to any number of treatments and does not rely on a prespecified form of the response or outcome functions. The proposed estimator is shown to reduce bias found in standard estimators that ignore the first-phase sample design, and can have improved efficiency compared to the inverse propensity weighted estimators. Results from simulation studies and from an empirical study of NHANES are presented.

Article information

Source
Electron. J. Statist., Volume 7 (2013), 2737-2761.

Dates
First available in Project Euclid: 18 November 2013

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1384783409

Digital Object Identifier
doi:10.1214/13-EJS856

Mathematical Reviews number (MathSciNet)
MR3138836

Zentralblatt MATH identifier
1283.62089

Keywords
Propensity score semiparametric treatment effects two-phase regression estimator

Citation

Yu, Cindy; Legg, Jason; Liu, Bin. Estimating multiple treatment effects using two-phase semiparametric regression estimators. Electron. J. Statist. 7 (2013), 2737--2761. doi:10.1214/13-EJS856. https://projecteuclid.org/euclid.ejs/1384783409


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