Causally-Guided Diffusion for Stable Feature Selection
Feature selection is fundamental to robust data-centric AI, but most existing methods optimize predictive performance under a single data distribution. This often selects spurious features that fail under distribution shifts. Motivated by principles from causal invariance, we study feature selection from a stability perspective and introduce Causally-Guided Diffusion for Stable Feature Selection (CGDFS). In CGDFS, we formalized feature selection as approximate posterior inference over feature subsets, whose posterior mass favors low prediction error and low cross-environment variance. Our framework combines three key insights: First, we formulate feature selection as stability-aware poste...
| Authors | Arun Vignesh Malarkkan, Xinyuan Wang, Kunpeng Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 March 2026 |
| Last updated at source | 21 March 2026 |
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