Under linearity and a continuity assumption, every feature attribution method is an integral against a signed measure, so the design choice reduces to picking the measure.
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
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Feature Attribution from First Principles
Under linearity and a continuity assumption, every feature attribution method is an integral against a signed measure, so the design choice reduces to picking the measure.