Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning
This work addresses the need for more interpretable and interaction-aware explanations in machine learning, particularly for counterfactual analysis, though it appears incremental as it builds on existing Shapley and Aumann-Shapley methods.
The authors tackled the problem of explaining counterfactual transitions in machine learning models by introducing Aumann-SHAP, a framework that decomposes interactions within a local hypercube to provide feature-level and instance-level explanations, with experiments on the German Credit and MNIST datasets showing robust results and better explanations than standard Shapley values.
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.