Null importance: Disentangling relevance for interpretable machine learning
Feature importance is central to interpretable machine learning, but the term "importance" encompasses several fundamentally different notions of relevance. We develop a unified perspective based on null importance: a population-level characterization of when a feature is irrelevant under a specified notion of relevance. We consider standard notions of null importance arising from marginal and conditional statistical relevance, predictive risk, functional invariance, and causal effects, and show how these notions answer different scientific questions. We illustrate the framework in two applications in which the distinction is particularly consequential: algorithmic fairness, where common ...
| Authors | Garvesh Raskutti, Kris Sankaran, Jiaxin Ye |
|---|---|
| Primary category | stat.ML |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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