Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning

TL;DR

Aumann-SHAP introduces a novel interaction-aware framework for decomposing counterfactual transitions in machine learning models, providing detailed feature interaction and contribution explanations that improve robustness over standard methods.

Contribution

It develops a new hypercube-based micro-game approach that captures feature interactions and individual contributions during counterfactual analysis, unifying with integrated gradients.

Findings

Aumann-LES yields more robust explanations than standard Shapley during counterfactuals.

The method effectively captures feature interactions in counterfactual transitions.

Experiments on German Credit and MNIST datasets demonstrate improved explanation quality.

Abstract

We introduce Aumann-SHAP, an interaction-aware framework that decomposes counterfactual transitions by restricting the model to a local hypercube connecting baseline and counterfactual features. Each hyper-cube is decomposed into a grid in order to construct an induced micro-player cooperative game in which elementary grid-step moves become players. Shapley and LES values on this TU-micro-game yield: (i) within-pot contribution of each feature to the interaction with other features (interaction explainability), and (ii) the contribution of each instance and each feature to the counterfactual analysis (individual and global explainability). In particular, Aumann-LES values produce individual and global explanations along the counterfactual transition. Shapley and LES values converge to the diagonal Aumann-Shapley (integrated-gradients) attribution method. Experiments on the German Credit dataset and MNIST data show that Aumann-LES produces robust results and better explanations than the standard Shapley value during the counterfactual transition.