Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
This work addresses causal inference for researchers handling decentralized RCT data, offering practical guidance but is incremental as it builds on existing federated learning and meta-analysis methods.
The paper tackled the problem of estimating treatment effects from decentralized data across centers by comparing three federated ATE estimators, finding that multi-shot federated learning leverages full data but requires more communication, with results validated via simulation.
We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outcome model (albeit requiring more communication). Focusing on Randomized Controlled Trials (RCTs), we derive the asymptotic variance of these estimators for linear models. Our results provide practical guidance on selecting the appropriate estimator for various scenarios, including heterogeneity in sample sizes, covariate distributions, treatment assignment schemes, and center effects. We validate these findings with a simulation study.