3 papers
stat.ML2026
Spectrally Deconfounded Gradient Boosting
Andrea Nava, Peter Bühlmann, Fabio Sigrist
Flexible machine-learning methods can be sensitive to hidden confounding: they may learn associations induced by unobserved confounders rather than stable signals. Spectral deconfo…
stat.ME2026
Stabilizing Variable Selection and Regression
Niklas Pfister, Evan G. Williams, Jonas Peters +2
We consider regression in which one predicts a response with a set of predictors across different experiments or environments. This is a common setup in many data-driven sc…
stat.ME2025
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml +1
We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment a…