# Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study

arXiv: https://arxiv.org/abs/1801.07318 · HTML: https://mlsanity.com/paper/1801.07318/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2018.

## Built on by (most cited first, 3 of 3)

- [Bayesian Neural Networks for Macroeconomic Analysis](https://mlsanity.com/paper/2211.04752/) (2022)
- [Non-linear dimension reduction in factor-augmented vector autoregressions](https://mlsanity.com/paper/2309.04821/) (2023)
- [Calibrated Derivative-Process Sensitivity for Gaussian-Process Variable Selection](https://mlsanity.com/paper/2609.33549/) (2026)

## Abstract (arXiv)

The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to summarize the relative importance of predictor variables. Methodologically, we develop the "RelATive cEntrality" (RATE) measure to prioritize candidate genetic variants that are not just marginally important, but whose associations also stem from significant covarying relationships with other variants in the data. We illustrate RATE through Bayesian Gaussian process regression, but the methodological innovations apply to other "black box" methods. It is known that nonlinear models often exhibit greater predictive accuracy than linear models, particularly for phenotypes generated by complex genetic architectures. With detailed simulations and two real data association mapping studies, we show that applying RATE enables an explanation for this improved performance.