Federated causal inference in heterogeneous observational data
Abstract
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 inferences 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 08, 2023
- Source ID
- 10.1002/sim.9868
Entities
People
- Allison Koenecke
- Joshua T Vogelstein
- Michael Powell
- Ruoxuan Xiong
- Susan Athey
- Zhu Shen
Organizations
- Cornell University
- Defense Advanced Research Projects Agency
- Emory University
- Harvard University
- Johns Hopkins University
- Microsoft Research
- Office of Naval Research
- Stanford University