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.

本文探讨了非对称Shapley值（ASV），一种流行的SHAP加法局部解释方法的变体。ASV提出了一种改进模型解释的方式，将已知的因果关系考虑进去，也被认为是测试模型预测中的不公平歧视的一种方式。同时，该方法对Shapley值中的对称性进行放松可能会导致直观相悖的结果，不适合根本原因分析。我们证明了ASV在广义可加模型（GAM）下具有良好性质，并通过理论结果支持了我们的观点。最后，我们使用梯度提升和深度学习模型在多个实际数据集上演示了非对称归因的使用情况，并将结果与不使用受限的模型族进行了比较。

不对称 Shapley 值在根本原因分析中的理论评估