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.

翻译：我们提出了Aumann-SHAP，这是一个交互感知框架，通过将模型限制在连接基准特征与反事实特征的局部超立方体内来分解反事实转换过程。每个超立方体被分解为一个网格，以构建一个诱导的微观参与者合作博弈，其中基本的网格步进移动成为博弈参与者。在此TU微观博弈上计算的Shapley值和LES值可得到：(i) 每个特征在与其他特征交互过程中的内部贡献（交互可解释性），以及(ii) 每个实例及每个特征对反事实分析的贡献（个体与全局可解释性）。特别地，Aumann-LES值能沿着反事实转换路径生成个体与全局解释。Shapley值与LES值收敛于对角线Aumann-Shapley（积分梯度）归因方法。在德国信用数据集和MNIST数据上的实验表明，在反事实转换过程中，Aumann-LES比标准Shapley值具有更强的鲁棒性并能提供更好的解释。