Federated Causal Inference in Heterogeneous Observational Data
This addresses the challenge of causal inference in federated settings for healthcare or similar domains, but it is incremental as it adapts existing propensity score methods to a federated context.
The paper tackled the problem of estimating treatment effects from observational data distributed across multiple sites with privacy constraints and heterogeneity, by developing federated methods that compute local summary statistics and aggregate them to obtain consistent and asymptotically normal estimators, as validated on two large medical claims databases.
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.