ShapShift explains prediction shifts by attributing them to changes in conditional probabilities of tree-defined subgroups via conditional Shapley values, with exact computation for single trees and surrogate extensions for other models.
Higher-order explanations of graph neural networks via relevant walks.IEEE transactions on pattern analysis and machine intelligence, 44(11):7581– 7596
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ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values
ShapShift explains prediction shifts by attributing them to changes in conditional probabilities of tree-defined subgroups via conditional Shapley values, with exact computation for single trees and surrogate extensions for other models.