6 papers
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…
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. Although a rich body of causal inferenc…
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…
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…
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…
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…