2 citations · 3 across the 10 of their papers we have counts for
12 papers
Causal Perspectives on Network Meta-Analysis
Ahmed Boughdiri, Francisco Andrade, Clément Berenfeld +1
Pairwise and network meta-analyses occupy the highest tier of evidence-based medicine and routinely inform clinical guidelines and healthcare decision-making. Current approaches ty…
Estimating treatment duration effects via clone-censor-weight: a breast cancer case study
Charlotte Voinot, Noémie Simon-Tillaux, Emma Torrini +4
In this work, we study the estimation of treatment duration effects in observational survival data, where treatment and covariate histories evolve over time and longer observed dur…
Set-Valued Policy Learning
Laura Fuentes-Vicente, Mathieu Even, Gaëlle Dormion +3
Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. However, when multiple treatments yield…
Preference-based Conditional Treatment Effects and Policy Learning
Dovid Parnas, Mathieu Even, Julie Josse +1
We introduce a new preference-based framework for conditional treatment effect estimation and policy learning, built on the Conditional Preference-based Treatment Effect (CPTE). CP…
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 Evaluation of Membership Inference Attacks
Mathieu Even, Clément Berenfeld, Linus Bleistein +3
Membership Inference Attacks (MIAs) aim to distinguish training points (members) from unseen data (non-members), and are widely used to quantify memorization and assess privacy ris…
Policy learning under constraint: Maximizing a primary outcome while controlling an adverse event
Laura Fuentes-Vicente, Mathieu Even, Gaelle Dormion +2
A medical policy aims to support decision-making by mapping patient characteristics to individualized treatment recommendations. Standard approaches typically optimize a single out…
A Unified Framework for the Transportability of Population-Level Causal Measures
Ahmed Boughdiri, Clément Berenfeld, Julie Josse +1
Generalization methods offer a powerful solution to one of the key drawbacks of randomized controlled trials (RCTs): their limited representativeness. By enabling the transport of…
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…
Model Agnostic Differentially Private Causal Inference
Christian Janos Lebeda, Mathieu Even, Aurélien Bellet +1
Estimating causal effects from observational data is essential in fields such as medicine, economics and social sciences, where privacy concerns are paramount. We propose a general…
Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis
Mathieu Even, Julie Josse
Quantifying causal effects in the presence of complex and multivariate outcomes remains a key challenge in treatment evaluation. For hierarchical multivariate outcomes, the FDA rec…
Treatment Effect Estimation in Causal Survival Analysis: Practical Recommendations
Charlotte Voinot, Clément Berenfeld, Imke Mayer +2
The restricted mean survival time (RMST) difference offers an interpretable causal contrast to estimate the treatment effect for time-to-event outcomes, yet a wide range of availab…