A simple algorithm for global sensitivity analysis with Shapley effects
Abstract
Global sensitivity analysis aims at measuring the relative importance of different variables or groups of variables for the variability of a quantity of interest. Among several sensitivity indices, so-called Shapley effects have recently gained popularity mainly because the Shapley effects for all the individual variables are summed up to the overall variance, which gives a better interpretability than the classical sensitivity indices called main effects and total effects. In this paper, assuming that all the input variables are independent, we introduce a quite simple Monte Carlo algorithm to estimate the Shapley effects for all the individual variables simultaneously, which drastically simplifies the existing algorithms proposed in the literature. We present a short Matlab implementation of our algorithm and show some numerical results. A possible extension to the case where the input variables are dependent is also discussed.
Cite
@article{arxiv.2009.00874,
title = {A simple algorithm for global sensitivity analysis with Shapley effects},
author = {Takashi Goda},
journal= {arXiv preprint arXiv:2009.00874},
year = {2021}
}