A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources

Xiaoqing (Ellen) Tan · Chung-Chou H. Chang · Ling Zhou · Lu Tang

Hall E #637

Keywords: [ APP: Health ] [ SA: Accountability, Transparency and Interpretability ] [ SA: Privacy-preserving Statistics and Machine Learning ] [ MISC: Causality ]

[ Abstract ]
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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT
Spotlight presentation: Miscellaneous Aspects of Machine Learning/Reinforcement Learning
Wed 20 Jul 1:30 p.m. PDT — 3 p.m. PDT


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.

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