Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
通过双阶段扰动检验实现必要性与充分性特征归因
We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests. Despite their utility, FAMs struggle to distinguish the contributions of different features, when their prediction changes are similar after perturbation. To enhance FAMs' discriminative power, we introduce Feature Attribution with Necessity and Sufficiency (FANS), which find a neighborhood of…