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)
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
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
Experiments demonstrate that SNRE learns region pairs with strong sufficiency-necessity performance, robust explanation behavior, and practical utility for model analysis.