Work

Individual variable priority: a model-independent local gradient method for variable importance

Year: 2025

Type: article

Abstract: Traditional variable importance measures quantify overall feature contributions but often overlook individual-level heterogeneity. Several new procedures attempt to address this limitation but remain model dependent and may introduce biases. We propose individual variable priority (iVarPro), an extension of the Variable Priority (VarPro) framework, which uses rule-based, data-driven partitioning to estimate the gradient of the conditional mean function. By focusing on gradients, iVarPro captures how small perturbations in a variable influence an individual's outcome, providing a more precise and interpretable measure of importance. To demonstrate its advantages, we conducted simulations and analyzed a real-world survival dataset. Our results show that iVarPro more accurately captures the true functional relationship by flexibly leveraging local samples.

Source: Artificial Intelligence Review

DOI: 10.1007/s10462-025-11339-y

Authors: Min Lu, Hemant Ishwaran

Institution: University of Miami

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of General Medical Sciences