Regional Explanations via Causal Sufficiency and Necessity

2026-09-15

AI Summary

本文提出因果充分必要区域解释框架(SNRE)，通过学习输入和输出区域来解决模型预测行为的区域级特征问题，使用概率必要性和充分性度量进行优化。

Abstract

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.

Problem

• Model Explainability
• Causal Sufficiency and Necessity
• Regional Explanations
• Stochastic Interventions
• Feature Importance

Innovation

• Causal Sufficiency and Necessity
• Regional Explanations
• Probability of Necessity and Sufficiency (PNS)
• Stochastic Interventions
• Differentiable Finite-sample Estimator