Web: http://arxiv.org/abs/2103.06261

June 17, 2022, 1:11 a.m. | Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang

cs.LG updates on arXiv.org arxiv.org

Accurately estimating personalized treatment effects within a study site
(e.g., a hospital) has been challenging due to limited sample size.
Furthermore, privacy considerations and lack of resources prevent a site from
leveraging subject-level data from other sites. We propose a tree-based model
averaging approach to improve the estimation accuracy of conditional average
treatment effects (CATE) at a target site by leveraging models derived from
other potentially heterogeneous sites, without them sharing subject-level data.
To our best knowledge, there is no …

arxiv data data sources ml model personalized treatment tree

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY