A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
Abstract
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 established model averaging approach for distributed data with a focus on improving the estimation of treatment effects. Specifically, under distributed data networks, our framework provides an interpretable tree-based ensemble of CATE estimators that joins models across study sites, while actively modeling the heterogeneity in data sources through site partitioning. The performance of this approach is demonstrated by a real-world study of the causal effects of oxygen therapy on hospital survival rate and backed up by comprehensive simulation results.
Cite
@article{arxiv.2103.06261,
title = {A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources},
author = {Xiaoqing Tan and Chung-Chou H. Chang and Ling Zhou and Lu Tang},
journal= {arXiv preprint arXiv:2103.06261},
year = {2022}
}
Comments
Accepted at ICML 2022. Previously titled "A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources"