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A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. (arXiv:2103.06261v3 [stat.ML] UPDATED)
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 …