Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects

Federated learning of causal estimands may greatly improve estimation efficiency by aggregating estimates from multiple study sites, but robustness to extreme estimates is vital for maintaining consistency. We develop a federated adaptive causal estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a target population of interest. Our strategy is communication-efficient and privacy-preserving and allows for flexibility in the specification of the target population. Our method accounts for site-level heterogeneity in the distribution of covariates through density ratio weighting. To safely aggregate estimates from all sites and avoid negative transfer, we introduce an adaptive procedure of weighing the estimators constructed using data from the target and source populations through a penalized regression on the influence functions, which achieves 1) consistency and 2) optimal efficiency. We illustrate FACE by conducting a comparative effectiveness study of BNT162b2 (Pfizer) and mRNA-1273 (Moderna) vaccines on COVID-19 outcomes in U.S. veterans using electronic health records from five VA sites.