Valid Feature-Level Inference for Tabular Foundation Models via the Conditional Randomization Test

Mohamed Salem

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arXiv · 2603.06609

Valid Feature-Level Inference for Tabular Foundation Models via the Conditional Randomization Test

Mohamed Salem

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Abstract

Modern machine learning models are highly expressive but notoriously difficult to analyze statistically. In particular, while black-box predictors can achieve strong empirical performance, they rarely provide valid hypothesis tests or p-values for assessing whether individual features contain information about a target variable. This article presents a practical approach to feature-level hypothesis testing that combines the Conditional Randomization Test (CRT) with TabPFN, a probabilistic foundation model for tabular data. The resulting procedure yields finite-sample valid p-values for conditional feature relevance, even in nonlinear and correlated settings, without requiring model retraining or parametric assumptions.

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BibTeX RIS

Mohamed Salem. 2026-02-19. Valid Feature-Level Inference for Tabular Foundation Models via the Conditional Randomization Test. https://arxiv.org/abs/2603.06609

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