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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 fromleveraging subject-level data from other sites. We propose a tree-based modelaveraging approach to improve the estimation accuracy of conditional averagetreatment effects (CATE) at a target site by leveraging models derived fromother potentially heterogeneous sites, without them sharing subject-level data.To our best knowledge, there is no established model averaging approach fordistributed data with a focus on improving the estimation of treatment effects.Specifically, under distributed data networks, our framework provides aninterpretable tree-based ensemble of CATE estimators that joins models acrossstudy sites, while actively modeling the heterogeneity in data sources throughsite partitioning. The performance of this approach is demonstrated by areal-world study of the causal effects of oxygen therapy on hospital survivalrate and backed up by comprehensive simulation results.

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