基于因果充分性与必要性的区域解释

中文摘要 AI 辅助

模型可解释性对于理解和信任机器学习模型至关重要。现有的可解释人工智能方法通常通过特征重要性、反事实解释或规则来解释预测。然而，关于预测行为何时发生且仅当何时发生的区域级刻画仍鲜有探索。本文提出了因果充分且必要的区域解释（SNRE），这是一个学习输入区域 $A$ 和输出区域 $B$ 的框架，使得属于 $A$ 对于模型输出落入 $B$ 既是充分的也是必要的。受经典的必要性与充分性概率（PNS）的启发，我们通过随机干预制定了区域级 PNS 度量，并推导出一个可微的有限样本估计器用于优化。SNRE 使用显式且可解释的代数区域族以及可学习的特征掩码对输入-输出区域对进行参数化，在表达性与可解释性之间取得平衡。实验表明，SNRE 学习到的区域对具有强充分性-必要性性能、稳健的解释行为，并对模型分析具有实际效用。

英文摘要

Model explainability is essential for understanding and trusting machine learning models. Existing explainable AI methods often explain predictions through feature importance, counterfactual explanations, or rules. However, a region-level characterization of when and only when a prediction behavior arises remains less explored. This paper proposes Causal Sufficient and Necessary Regional Explanations (SNRE), a framework that learns an input region $A$ and output region $B$ such that membership in $A$ is both sufficient and necessary for the model output to fall in $B$. Motivated by the classical Probability of Necessity and Sufficiency (PNS), we formulate a region-level PNS measure through stochastic interventions and derive a differentiable finite-sample estimator for optimization. SNRE parameterizes the input-output region pair with explicit and interpretable algebraic region families, together with a learnable feature mask, balancing expressiveness and interpretability. Experiments demonstrate that SNRE learns region pairs with strong sufficiency-necessity performance, robust explanation behavior, and practical utility for model analysis.

发表机构

• Guangdong Polytechnic Normal University（广东技术师范大学）
• Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
• Guangdong University of Technology（广东工业大学）

机构由 AI 辅助整理，请以论文原文为准。