AI RESEARCH
Causally-Guided Diffusion for Stable Feature Selection
arXiv CS.AI
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ArXi:2603.20930v1 Announce Type: cross 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