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

Accurately estimating personalized treatment effects within a single study has been challenging due to the limited sample size. Here we propose a tree-based model averaging approach to improve the estimation efficiency of conditional average treatment effects concerning the population of a target research site by leveraging models derived from potentially heterogeneous populations of other sites, but without them sharing individual-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. Under distributed data networks, we develop an efficient and interpretable tree-based ensemble of personalized treatment effect estimators to join results across hospital sites, while actively modeling for the heterogeneity in data sources through site partitioning. The efficiency of this approach is demonstrated by a study of causal effects of oxygen saturation on hospital mortality and backed up by comprehensive numerical results.