Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu
Abstract
We consider off-policy evaluation (OPE) in continuous treatment settings, such as personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new treatment decision rule using historical data generated by a different decision rule. Most existing works on OPE focus on discrete treatment settings. To handle continuous treatments, we develop a novel estimation method for OPE using deep jump learning. The key ingredient of our method lies in adaptively discretizing the treatment space using deep discretization, by leveraging deep learning and multi-scale change point detection. This allows us to apply existing OPE methods in discrete treatments to handle continuous treatments. Our method is further justified by theoretical results, simulations, and a real application to Warfarin Dosing.
BibTeX
@inproceedings{
cai2021deep,
title={Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings},
author={Hengrui Cai and Chengchun Shi and Rui Song and Wenbin Lu},
booktitle={Advances in Neural Information Processing Systems},
editor={A. Beygelzimer and Y. Dauphin and P. Liang and J. Wortman Vaughan},
year={2021},
url={https://openreview.net/forum?id=0hJ-U3aqUDf}
}