Federated Causal Inference in Heterogeneous Observational Data

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Tác giả: Susan Athey, Allison Koenecke, Michael Powell, Zhu Shen, Joshua T Vogelstein, Ruoxuan Xiong

Ngôn ngữ: eng

Ký hiệu phân loại: 511.34 Model theory

Thông tin xuất bản: 2021

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Bộ sưu tập: Metadata

ID: 167484

 We are interested in estimating the effect of a treatment applied to individuals at multiple sites, where data is stored locally for each site. Due to privacy constraints, individual-level data cannot be shared across sites
  the sites may also have heterogeneous populations and treatment assignment mechanisms. Motivated by these considerations, we develop federated methods to draw inference on the average treatment effects of combined data across sites. Our methods first compute summary statistics locally using propensity scores and then aggregate these statistics across sites to obtain point and variance estimators of average treatment effects. We show that these estimators are consistent and asymptotically normal. To achieve these asymptotic properties, we find that the aggregation schemes need to account for the heterogeneity in treatment assignments and in outcomes across sites. We demonstrate the validity of our federated methods through a comparative study of two large medical claims databases.
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