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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…
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…