Generalizability vs. Counterfactual Explainability Trade-Off

Abstract

In this work, we investigate the relationship between model generalization and counterfactual explainability in supervised learning. We introduce the notion of -valid counterfactual probability (-VCP) -- the probability of finding perturbations of a data point within its -neighborhood that result in a label change. We provide a theoretical analysis of -VCP in relation to the geometry of the model's decision boundary, showing that -VCP tends to increase with model overfitting. Our findings establish a rigorous connection between poor generalization and the ease of counterfactual generation, revealing an inherent trade-off between generalization and counterfactual explainability. Empirical results validate our theory, suggesting -VCP as a practical proxy for quantitatively characterizing overfitting.

Tasks

Reproductions