Multiply Robust Federated Estimation of Targeted Average Treatment Effects

Autor:	Han, Larry, Shen, Zhu, Zubizarreta, Jose
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Mathematics - Statistics Theory Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	Federated or multi-site studies have distinct advantages over single-site studies, including increased generalizability, the ability to study underrepresented populations, and the opportunity to study rare exposures and outcomes. However, these studies are challenging due to the need to preserve the privacy of each individual's data and the heterogeneity in their covariate distributions. We propose a novel federated approach to derive valid causal inferences for a target population using multi-site data. We adjust for covariate shift and covariate mismatch between sites by developing multiply-robust and privacy-preserving nuisance function estimation. Our methodology incorporates transfer learning to estimate ensemble weights to combine information from source sites. We show that these learned weights are efficient and optimal under different scenarios. We showcase the finite sample advantages of our approach in terms of efficiency and robustness compared to existing approaches. Comment: Accepted at NeurIPS 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2309.12600 Zobrazit plný text záznamu View this record from Arxiv