Federated Causal Inference: Multi-Centric ATE Estimation beyond
Meta-Analysis
Federated Causal Inference: Multi-Centric ATE Estimation beyond
Meta-Analysis
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 …