  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 as the number of features increases. Various approaches,
including ApproSemivalue, KernelSHAP, and FastSHAP, have been explored to
expedite the computation. In our analysis of existing approaches, we observe
that stochastic estimators can be unified as a linear transformation of
randomly summed values from 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.
