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Weighted Gaussian Process for Estimating Treatment Effect

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.


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


  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: June 28, 2018
Submitted by Russ Greiner

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