2 citations · 3 across the 4 of their papers we have counts for
5 papers
Handling Covariate Mismatch in Collaborative Linear Prediction
Alexis Ayme, Rémi Khellaf
Training predictive models across multiple centers typically assumes that all centers collect the same set of covariates. In practice, however, they may record different features o…
Principled Federated Random Forests for Heterogeneous Data
Rémi Khellaf, Erwan Scornet, Aurélien Bellet +1
Random Forests (RF) are among the most powerful and widely used predictive models for centralized tabular data, yet few methods exist to adapt them to the federated learning settin…
Causal Meta-Analysis: Rethinking the Foundations of Evidence-Based Medicine
Clément Berenfeld, Ahmed Boughdiri, Bénédicte Colnet +5
Meta-analysis, by synthesizing effect estimates from multiple studies conducted in diverse settings, stands at the top of the evidence hierarchy in clinical research. Yet, conventi…
Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregation
Rémi Khellaf, Aurélien Bellet, Julie Josse
Causal inference typically assumes centralized access to individual-level data. Yet, in practice, data are often decentralized across multiple sites, making centralization infeasib…
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
Rémi Khellaf, Aurélien Bellet, Julie Josse
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) es…