Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction

• URL: http://arxiv.org/abs/2508.08724v1
• Date: Tue, 12 Aug 2025 08:10:54 GMT
• Title: Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
• Authors: Joseph Paillard, Antoine Collas, Denis A. Engemann, Bertrand Thirion,
• Abstract summary: We introduce Hierarchical-CPI, a model-agnostic variable importance measure that frames the inference problem as the discovery of groups of variables.<n>By exploring subgroups along a hierarchical tree, it remains computationally tractable, yet also enjoys explicit family-wise error rate control.<n>Its effectiveness is demonstrated in two neuroimaging datasets.
• Score: 35.94354098982828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in machine learning have greatly expanded the repertoire of predictive methods for medical imaging. However, the interpretability of complex models remains a challenge, which limits their utility in medical applications. Recently, model-agnostic methods have been proposed to measure conditional variable importance and accommodate complex non-linear models. However, they often lack power when dealing with highly correlated data, a common problem in medical imaging. We introduce Hierarchical-CPI, a model-agnostic variable importance measure that frames the inference problem as the discovery of groups of variables that are jointly predictive of the outcome. By exploring subgroups along a hierarchical tree, it remains computationally tractable, yet also enjoys explicit family-wise error rate control. Moreover, we address the issue of vanishing conditional importance under high correlation with a tree-based importance allocation mechanism. We benchmarked Hierarchical-CPI against state-of-the-art variable importance methods. Its effectiveness is demonstrated in two neuroimaging datasets: classifying dementia diagnoses from MRI data (ADNI dataset) and analyzing the Berger effect on EEG data (TDBRAIN dataset), identifying biologically plausible variables.

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