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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
publications
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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talks
Conference on Automatic Learning (CAp 24)
I gave a talk on Federated Causal Inference and presented a poster at the Conference for Automatic Learning. Poster download available soon! Conference Program here
Journées de Biostatistiques 2023
I gave a talk about the use of Instrumental Variables estimators in Causal Inference, based on my internship prior to my PhD at Inria, in collaboration with Quinten Health. Conference Program here
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.
Journées des Statistiques 2024
I gave a talk on Federated Causal Inference in the Causal Inference session of the Statistics Days of the French Society for Statistics (SFDS). Conference Program here
teaching
Introduction to Probabilistic Graphical Models
Teaching Assistant, M2 Mathématiques / Vision / Apprentissage, 2024