Is VARS more intuitive and efficient than Sobol' indices?

TL;DR

This paper critically compares VARS and Sobol' indices, finding that VARS lacks a clear importance measure and does not outperform Sobol' in computational efficiency, thus complementing rather than replacing traditional methods.

Contribution

The paper provides a critical review of VARS, highlighting its limitations and clarifying its role alongside classic variance-based sensitivity analysis methods.

Findings

VARS lacks a clear definition of factor importance.

VARS does not demonstrate superior computational efficiency.

VARS complements traditional variance-based methods.

Abstract

The Variogram Analysis of Response Surfaces (VARS) has been proposed by Razavi and Gupta as a new comprehensive framework in sensitivity analysis. According to these authors, VARS provides a more intuitive notion of sensitivity and it is much more computationally efficient than Sobol' indices. Here we review these arguments and critically compare the performance of VARS-TO, for total-order index, against the total-order Jansen estimator. We argue that, unlike classic variance-based methods, VARS lacks a clear definition of what an "important" factor is, and show that the alleged computational superiority of VARS does not withstand scrutiny. We conclude that while VARS enriches the spectrum of existing methods for sensitivity analysis, especially for a diagnostic use of mathematical models, it complements rather than substitutes classic estimators used in variance-based sensitivity analysis.