Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis

作者：Rémi Khellaf, Aurélien Bellet, Julie Josse · 发表于：arXiv (Cornell University) · 年份：2024 · DOI：10.48550/arxiv.2410.16870 · 被引用次数：2 · 研究领域：Advanced Causal Inference Techniques、Statistical Methods and Inference、Statistical Methods and Bayesian Inference

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.