3 papers
stat.ML2026
Aggregate Models, Not Explanations: Improving Feature Importance Estimation
Joseph Paillard, Angel Reyero Lobo, Denis A. Engemann +1
Feature-importance methods show promise in transforming machine learning models from predictive engines into tools for scientific discovery. However, due to data sampling and algor…
cs.LG2024
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
Joseph Paillard, Angel Reyero Lobo, Vitaliy Kolodyazhniy +2
Causal machine learning holds promise for estimating individual treatment effects from complex data. For successful real-world applications of machine learning methods, it is of pa…
stat.ME2024
When Knockoffs fail: diagnosing and fixing non-exchangeability of Knockoffs
Alexandre Blain, Angel Reyero Lobo, Julia Linhart +2
Knockoffs are a popular statistical framework that addresses the challenging problem of conditional variable selection in high-dimensional settings with statistical control. Such s…