Trustworthy Feature Importance Avoids Unrestricted Permutations
arXiv stat.ML / 4/14/2026
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Key Points
- Existing feature-importance methods that rely on unrestricted permutations are flawed because they introduce extrapolation errors that can affect essentially all non-trivial variable importance approaches.
- The authors argue that extrapolation failures are a common underlying problem, not an edge case limited to a specific method.
- They propose three new strategies—(1) conditional model reliance, (2) Knockoffs with Gaussian transformation, and (3) restricted ALE plot designs—to address the extrapolation issue.
- The paper reports theoretical and numerical results indicating these strategies can reduce or eliminate extrapolation errors, improving trustworthiness of feature importance estimates.
