提出几何感知的反事实解释框架，更准确分解特征贡献。
Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning
- 以局部超立方体构造博弈模型，用微步移动作为玩家计算贡献。
- 在多个数据集上纠正了传统方法的误判，提升解释准确性。
- 适合需要高精度特征归因的机器学习可解释性研究者。
我们提出Aumann-SHAP，一个考虑交互作用的反事实解释框架。该方法将基线与反事实特征间的转移限制在局部超立方体内，并将其离散化为网格，构建诱导的微观合作博弈，其中每个网格步移视为玩家。在此基础上计算的Shapley和LES值能生成几何感知的内部归因，在网格细化时收敛到对角Aumann-Shapley/集成梯度极限，并退化为m=1时的等分Shapley。对于固定交互阶数，存在多项式时间的闭式解。在具有已知真值的合成基准上，等分Shapley存在不可消除偏差，而Aumann-SHAP收敛至正确分解。在German Credit数据集中，交互几何改变特征优先级排序的比例达12.3%。在UCI Heart Disease中，等分方法错误将胆固醇抑制因子识别为正向贡献，这是符号错误，Aumann-SHAP成功纠正。在MNIST上，基于博弈论的归因仅需3.5倍更少的编辑即可达到目标置信度，且微博弈Shapley在所有预算下效率最优。
原文摘要 · Abstract (English)
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 hypercube is discretized into a grid 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 geometry-aware within-pot attributions that converge to the diagonal Aumann--Shapley / Integrated Gradients limit under grid refinement, and recover equal-split Shapley as the degenerate $m=1$ special case. An exact grid-state closed form gives polynomial-time computation for fixed interaction order. On a synthetic benchmark with known ground truth, equal-split Shapley carries an irreducible bias while Aumann-SHAP converges to the correct decomposition. On German Credit, interaction geometry changes feature priority rankings in $12.3\%$ of instances. On UCI Heart Disease, equal-split misattributes a cholesterol suppressor as a positive contributor, which is a sign error Aumann-SHAP corrects. On MNIST, game-theoretic attribution reaches target confidence with $3.5\times$ fewer edits than magnitude-based ordering, with micro-game Shapley achieving the best efficiency across all budgets.
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