Judith Abécassis — accepted AI-conference papers

2025

Double Debiased Machine Learning for Mediation Analysis with Continuous Treatments

Houssam Zenati, Judith Abécassis, Julie Josse, Bertrand Thirion

AISTATS 2025poster

Uncovering causal mediation effects is of significant value to practitioners who aim to isolate treatment effects from potential mediator effects. We propose a double machine learning (DML) algorithm for mediation analysis that supports continuous treatments. To estimate the target mediated response…

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2025

Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks

Julie Alberge, Vincent Maladiere, Olivier Grisel, Judith Abécassis, Gael Varoquaux

AISTATS 2025poster

When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis —known as *time-to-event analysis*— focuses on predicting the time until an event of interest occurs. Multiple classes of outcomes lead to a classification variant: predictin…

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