On Shapley Value for Measuring Importance of Dependent Inputs
Art B. Owen, Clémentine Prieur · SIAM/ASA Journal on Uncertainty Quantification · 2017
This paper makes the case for using Shapley value to quantify the importance of random input variables to a function. Alternatives based on the ANOVA decomposition can run into conceptual and computational problems when the input variables are dependent. Our main goal here is to show that Shapley value removes the conceptual problems. We do this with some simple examples where Shapley value leads to intuitively reasonable nearly closed form answers.