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Pages
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About me
Posts
publications
Federated Causal Inference: Multi-Centric ATE Estimation beyond Meta-Analysis
Published in Arxiv, 2024
In this paper, 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.
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). Slides coming soon! Conference Program here
teaching
Introduction to Probabilistic Graphical Models
Teaching Assistant, M2 Mathématiques / Vision / Apprentissage, 2024