1 citations · 1 across the 3 of their papers we have counts for
4 papers
Sequential Conditional Independence Testing with Machine Learning Models
Angel Reyero-Lobo, Michele Meziu, Sebastian Uriel Arias +1
Conditional independence testing is a ubiquitous problem in scientific discovery. The widely employed model-X assumption shifts the modelling burden from the dependence of the outp…
Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees
Angel Reyero-Lobo, Bertrand Thirion, Pierre Neuvial
Conditional independence testing (CIT) is essential for reliable scientific discovery. It prevents spurious findings and enables controlled feature selection. Recent CIT methods ha…
A Principled Approach for Defining and Comparing Variable Importance Measures
Angel Reyero-Lobo, Pierre Neuvial, Bertrand Thirion
Variable importance measures (VIMs) aim to quantify the contribution of each input covariate to the predictability of a given output. With the growing interest in explainable AI, n…
Conditional Feature Importance revisited: Double Robustness, Efficiency and Inference
Angel Reyero-Lobo, Pierre Neuvial, Bertrand Thirion
Conditional Feature Importance (CFI) is a classical variable importance measure that accounts for the relationship between the studied feature and the others. However, CFI has not…