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

arXiv:2603.06609v1 Announce Type: new 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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Mohamed Salem · · 1 min read · 8 views
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arXiv:2603.06609v1 Announce Type: new 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.

Executive Summary

This article presents a novel approach to feature-level hypothesis testing for tabular foundation models, leveraging the Conditional Randomization Test (CRT) in conjunction with TabPFN, a probabilistic foundation model for tabular data. The proposed method enables the derivation of finite-sample valid p-values for conditional feature relevance, even in complex settings, without necessitating model retraining or parametric assumptions. This breakthrough has significant implications for the validity of hypothesis testing in machine learning models, particularly in black-box scenarios where empirical performance is often prioritized over interpretability. The research's practical and policy implications are substantial, as it addresses a pressing need in the field for robust and actionable feature-level inference.

Key Points

  • ▸ The Conditional Randomization Test (CRT) is combined with TabPFN for feature-level hypothesis testing.
  • ▸ The resulting procedure yields finite-sample valid p-values for conditional feature relevance.
  • ▸ The method is applicable in nonlinear and correlated settings without requiring model retraining or parametric assumptions.

Merits

Statistical Validity

The proposed method ensures the derivation of finite-sample valid p-values for conditional feature relevance, addressing a critical shortcoming in existing machine learning approaches.

Interpretability

The research provides a practical solution for feature-level inference, enabling the identification of relevant features in complex machine learning models.

Flexibility

The method is applicable in a wide range of settings, including nonlinear and correlated scenarios, without necessitating model retraining or parametric assumptions.

Demerits

Computational Complexity

The CRT-based approach may incur increased computational costs, particularly for large datasets or complex models.

Assumptions

While the method does not require parametric assumptions, it may still rely on certain assumptions about the data distribution or model behavior.

Expert Commentary

The article's contribution to the field of machine learning is substantial, as it addresses a pressing need for robust and actionable feature-level inference. The proposed method's ability to yield finite-sample valid p-values for conditional feature relevance, even in complex settings, is a critical breakthrough. However, the computational complexity and assumptions required by the CRT-based approach may limit its applicability in certain scenarios. Nevertheless, the research's potential to improve the interpretability and validity of machine learning models makes it a valuable contribution to the field.

Recommendations

  • ✓ Future research should investigate the extension of the proposed method to other types of data, such as text or image data.
  • ✓ The development of more efficient algorithms or approximation methods could help mitigate the computational complexity of the CRT-based approach.

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