تقرير
A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources
| العنوان: | A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources |
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| المؤلفون: | Tan, Xiaoqing, Chang, Chung-Chou H., Zhou, Ling, Tang, Lu |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology |
| الوصف: | 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. Comment: Accepted at ICML 2022. Previously titled "A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources" |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2103.06261 |
| رقم الأكسشن: | edsarx.2103.06261 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |