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

• URL: http://arxiv.org/abs/2603.06609v1
• Date: Thu, 19 Feb 2026 05:58:11 GMT
• Title: Valid Feature-Level Inference for Tabular Foundation Models via the Conditional Randomization Test
• Abstract summary: This article presents a practical approach to feature-level hypothesis testing that combines the Conditional Randomization Test (CRT) with TabPFN.<n>The resulting procedure yields finite-sample valid p-values for conditional feature relevance, even in nonlinear and correlated settings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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