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Masking some input variables of a deep neural network (DNN) and computingoutput changes on the masked input sample represent a typical way to computeattributions of input variables in the sample. People usually mask an inputvariable using its baseline value. However, there is no theory to examinewhether baseline value faithfully represents the absence of an input variable,\emph{i.e.,} removing all signals from the input variable. Fortunately, recentstudies show that the inference score of a DNN can be strictly disentangledinto a set of causal patterns (or concepts) encoded by the DNN. Therefore, wepropose to use causal patterns to examine the faithfulness of baseline values.More crucially, it is proven that causal patterns can be explained as theelementary rationale of the Shapley value. Furthermore, we propose a method tolearn optimal baseline values, and experimental results have demonstrated itseffectiveness.

Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN?