Multiply Robust Federated Estimation of Targeted Average Treatment Effects

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. Recent methodological developments have focused on privacy-preserving estimation strategies. These strategies typically involve sharing summary-level information from multiple data sources [26, 27, 12, 13, 18] . However, they often require restrictive assumptions such as homogeneous data structures and model specifications (e.g., a common set of observed covariates measured using a common data model), which are not realistic in practice. To address these methodological gaps, we propose a multiply robust and privacy-preserving estimator that leverages multi-site information to estimate causal effects in a target population of interest.