Federated Causal Inference in Heterogeneous Observational Data

TL;DR

This paper develops federated causal inference methods for estimating average treatment effects across multiple sites with privacy constraints, heterogeneous populations, and treatment mechanisms, using local summary statistics and aggregation.

Contribution

It introduces novel federated approaches for causal inference that handle heterogeneity and privacy, with proven consistency and asymptotic normality.

Findings

Methods are validated on large medical claims databases.

Estimators are shown to be consistent and asymptotically normal.

Aggregation schemes effectively account for heterogeneity.

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 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.