Shapley effects for sensitivity analysis with dependent inputs: bootstrap and kriging-based algorithms

TL;DR

This paper advances sensitivity analysis by developing bootstrap and kriging-based algorithms for estimating Shapley effects with dependent inputs, providing confidence intervals and improved interpretability in complex models.

Contribution

It introduces bootstrap sampling for confidence intervals and a metamodel approach to efficiently estimate Shapley effects with dependent inputs.

Findings

Bootstrap methods effectively estimate confidence intervals.

Kriging metamodels reduce computational costs.

Shapley effects offer better interpretability with dependent inputs.

Abstract

In global sensitivity analysis, the well known Sobol' sensitivity indices aim to quantify how the variance in the output of a mathematical model can be apportioned to the different variances of its input random variables. These indices are based on the functional variance decomposition and their interpretation become difficult in the presence of statistical dependence between the inputs. However, as there is dependence in many application studies, that enhances the development of interpretable sensitivity indices. Recently, the Shapley values developed in the field of cooperative games theory have been connected to global sensitivity analysis and present good properties in the presence of dependencies. Nevertheless, the available estimation methods don't always provide confidence intervals and require a large number of model evaluation. In this paper, we implement a bootstrap sampling in the existing algorithms to estimate confidence intervals of the indice estimations. We also proposed to consider a metamodel in substitution of a costly numerical model. The estimation error from the Monte-Carlo sampling is combined with the metamodel error in order to have confidence intervals on the Shapley effects. Besides, we compare for different examples with dependent random variables the results of the Shapley effects with existing extensions of the Sobol' indices.