Before revision:  Measures of variable importance usually work by changing the value of one or more variables with the others held fixed and then recomputing the function of interest. That approach is problematic because it can create very unrealistic combinationsof predictors that never appear in practice or that were never present when the prediction function was being created. Our cohort refinement Shapley approach measures variable importance without using any data points that were not actually observed .
After revision:  Measures of variable importance usually work by changing the value of one or more variables with the others held fixed , potentially creating unlikely or even logically impossible combinations. Our cohort Shapley measure uses only observed data points. Instead of changing the value of a predictor we include or exclude subjects similar to the target subject on that predictor to form a similarity cohort. Then we apply Shapley value to the cohort averages. We connect variable importance measures from explainable AI to function decompositions from global sensitivity analysis. We introduce a squared cohort Shapley value that splits previously studied Shapley effects over subjects, consistent with a Shapley axiom .