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

Published in AISTATS, 2024

In this paper, we study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across studies, or data 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.

Recommended citation: Khellaf R., Bellet A., Josse J. (2024). "Federated Causal Inference: Multi-sources ATE estimation." Conference Article.
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Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation

Published in Arxiv, 2025

Causal inference typically assumes centralized access to individual-level data. Yet, in practice, data are often decentralized across multiple sites, making centralization infeasible due to privacy, logistical, or legal constraints. We address this by estimating the Average Treatment Effect (ATE) from decentralized observational data using federated learning, which enables inference through the exchange of aggregate statistics rather than individual-level data. We propose a novel method to estimate propensity scores in a (non-)parametric manner by computing a federated weighted average of local scores, using two theoretically grounded weighting schemes – Membership Weights (MW) and Density Ratio Weights (DW) – that balance communication efficiency and model flexibility. These federated scores are then used to construct two ATE estimators: the Federated Inverse Propensity Weighting estimator (Fed-IPW) and its augmented variant (Fed-AIPW). Unlike meta-analysis methods, which fail when any site violates positivity, our approach leverages heterogeneity in treatment assignment across sites to improve overlap. We show that Fed-IPW and Fed-AIPW perform well under site-level heterogeneity in sample sizes, treatment mechanisms, and covariate distributions, with theoretical analysis and experiments on simulated and real-world data highlighting their strengths and limitations relative to meta-analysis and related methods.

Recommended citation: Khellaf, R., Bellet, A., & Josse, J. (2025). Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation." Conference Article.
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Workshop at INRAE

Published:

Along with Charlotte Voinot, we taught an introduction to Causal Inference to the mathematics and biostatistics department of INRAE in Jouy-en-Josas campus.

teaching