In this work, we examine Asymmetric Shapley Values (ASV), a variant of the popular SHAP additive local explanation method. ASV proposes a way to improve model explanations incorporating known causal relations between variables, and is also considered as a way to test for unfair discrimination in model predictions. Unexplored in previous literature, relaxing symmetry in Shapley values can have counter-intuitive consequences for model explanation. To better understand the method, we first show how local contributions correspond to global contributions of variance reduction. Using variance, we demonstrate multiple cases where ASV yields counter-intuitive attributions, arguably producing incorrect results for root-cause analysis. Second, we identify generalized additive models (GAM) as a restricted class for which ASV exhibits desirable properties. We support our arguments by proving multiple theoretical results about the method. Finally, we demonstrate the use of asymmetric attributions on multiple real-world datasets, comparing the results with and without restricted model families using gradient boosting and deep learning models.

翻译：本研究考察了非对称沙普利值（ASV）——一种流行的SHAP加性局部解释方法的变体。ASV提出了一种改进模型解释的方法，该方法融合了变量间已知的因果关联，同时也被视为检测模型预测中不公平歧视的手段。文献中尚未探讨的是，放松沙普利值的对称性可能对模型解释产生反直觉的后果。为更深入理解该方法，我们首先展示了局部贡献如何与方差缩减的全局贡献相对应。利用方差，我们证明了在多种情形下ASV会产生反直觉的归因，可以说在根因分析中产生了错误结果。其次，我们识别出广义加性模型（GAM）是一类特殊的模型族，在该族中ASV展现出理想性质。我们通过证明该方法的多个理论结果来支持我们的论点。最后，我们在多个真实世界数据集上展示了非对称归因的使用，并对比了使用梯度提升和深度学习模型时限制与非限制模型族的结果。