Weighted Gaussian Process for Estimating Treatment Effect
- Junfeng Wen
- Negar Hassanpour, Department of Computing Science, University of Alberta
- Russ Greiner, Dept of Computing Science; PI of AICML
Estimating treatment effect is crucial in many fields, including but not limited to medicine, psychology and economics. Accurate estimation of treatment effect is difficult in most observational studies, as those collected examples are inevitably biased: distributions of sample covariates between treatment and control groups are misaligned due to experimental conditions or constraints. To address this issue, we borrow covariate shift correction techniques from the transfer machine learning community and incorporate them into weighted Gaussian process for effective bias correction. Our method can (1) correct sample bias, (2) predict both population and individual treatment effects, and (3) provide corresponding confidence intervals.
Citation
J. Wen, N. Hassanpour, R. Greiner. "Weighted Gaussian Process for Estimating Treatment Effect". December 2016.| Keywords: | covariate shift, machine learning, gaussian proces, rct |
| Category: | In Workshop |
| Web Links: | Workshop URL |
BibTeX
@misc{Wen+al:16,
author = {Junfeng Wen and Negar Hassanpour and Russ Greiner},
title = {Weighted Gaussian Process for Estimating Treatment Effect},
Booktitle = {NIPS "What if" Workshop},
year = 2016,
}Last Updated: February 11, 2020Submitted by Sabina P
