Shapley values have emerged as a widely accepted and trustworthy tool,
grounded in theoretical axioms, for addressing challenges posed by black-box
models like deep neural networks. However, computing Shapley values encounters
exponential complexity in the number of features. Various approaches, including
ApproSemivalue, KernelSHAP, and FastSHAP, have been explored to expedite the
computation. We analyze the consistency of existing works and conclude that
stochastic estimators can be unified as the linear transformation of importance
sampling of feature subsets. Based on this, we investigate the possibility of
designing simple amortized estimators and propose a straightforward and
efficient one, SimSHAP, by eliminating redundant techniques. Extensive
experiments conducted on tabular and image datasets validate the effectiveness
of our SimSHAP, which significantly accelerates the computation of accurate
Shapley values.

Shapley 值是一种广泛接受和可信赖的工具，用于解决深度神经网络等黑盒模型所带来的挑战。本研究分析了现有工作的一致性，并推断出随机估计器可以统一为特征子集重要性采样的线性变换。基于此，我们探讨设计简单的摊销估计器的可能性，并提出了一种简单高效的方法 ——SimSHAP，通过消除冗余技术。在表格和图像数据集上进行的大量实验证实了我们的 SimSHAP 的有效性，能够显著加速准确 Shapley 值的计算。