Measuring Variable Importance Using Proportional Inclusive Marginal Effects
Shapley-based measures are widely used for measuring variable importance in statistics and machine learning. However, these measures have a serious issue--a nonactive variable can get a positive importance measure when it is correlated with an active variable. Herin et al. recently proposed a measure called proportional marginal effects (PME) that mitigates this spurious effect. However, PME is developed for deterministic models and has high computational cost. In this article, we propose a new measure called proportional inclusive marginal effects (PRIME) that overcomes these two limitations of PME. The key idea in PRIME is to use a novel model-free variable selection technique to identi...
| Authors | Youngseo Cho, V. Roshan Joseph |
|---|---|
| Primary category | stat.ML |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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