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Attributing Cohen's d: Training Data Attribution for Disease-Related Effects in Normative Age Biomarkers

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

Normative age models are trained to predict chronological age in a nominally healthy cohort. Applied to patients, they deviate, and the gap between predicted and chronological age is read as disease risk. Here, we attribute the disease-related effect size of the age gap directly to individual training samples, rather than using a prediction-level loss as the attribution target. For Cohen's $d$, the resulting closed-form influence functional, validated against leave-one-out retraining, ranks training samples by their effect on held-out case-control separation. Across four diseases and two bioma

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