# Global Sensitivity Analysis with Dependence Measures

arXiv: https://arxiv.org/abs/1311.2483 · HTML: https://mlsanity.com/paper/1311.2483/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2013.

## Built on by (most cited first, 12 of 12)

- [Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure](https://mlsanity.com/paper/2206.06219/) (2022)
- [Goal-Oriented Sensitivity Analysis of Hyperparameters in Deep Learning](https://mlsanity.com/paper/2207.06216/) (2022)
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- [Sliced gradient-enhanced Kriging for high-dimensional function approximation](https://mlsanity.com/paper/2204.03562/) (2022)
- [The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels](https://mlsanity.com/paper/2403.07735/) (2024)
- [A new paradigm for global sensitivity analysis](https://mlsanity.com/paper/2409.06271/) (2024)
- [Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework](https://mlsanity.com/paper/2505.00220/) (2025)
- [Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification](https://mlsanity.com/paper/2506.13211/) (2025)
- [Sparse minimum Redundancy Maximum Relevance for feature selection](https://mlsanity.com/paper/2508.18901/) (2025)
- [Fourier Analysis on the Boolean Hypercube via Hoeffding Functional Decomposition](https://mlsanity.com/paper/2510.07088/) (2025)
- [Hybrid Uncertainty Sensitivity Analysis Based on the HSIC for High-Dimensional Responses with Aleatory--Epistemic Separation](https://mlsanity.com/paper/2606.14053/) (2026)
- [Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection](https://mlsanity.com/paper/2609.33549/) (2026)

## Abstract (arXiv)

Global sensitivity analysis with variance-based measures suffers from several theoretical and practical limitations, since they focus only on the variance of the output and handle multivariate variables in a limited way. In this paper, we introduce a new class of sensitivity indices based on dependence measures which overcomes these insufficiencies. Our approach originates from the idea to compare the output distribution with its conditional counterpart when one of the input variables is fixed. We establish that this comparison yields previously proposed indices when it is performed with Csiszar f-divergences, as well as sensitivity indices which are well-known dependence measures between random variables. This leads us to investigate completely new sensitivity indices based on recent state-of-the-art dependence measures, such as distance correlation and the Hilbert-Schmidt independence criterion. We also emphasize the potential of feature selection techniques relying on such dependence measures as alternatives to screening in high dimension.