Shapley Values (SVs) are widely used in summarizing item contributions and importance, but become computationally impossible for, say, 30-plus items. We will briefly introduce SVs and a trick for computation in variants of TURF, but focus on a new class of experimental designs that dramatically improve the accuracy of SV sampling approaches and make even 200 items feasible, for all problem types. We will also compare SVs to Johnson's Relative Weight Analysis for key driver regressions.
