为表格模型提供可验证的特征重要性检验方法
Valid Feature-Level Inference for Tabular Foundation Models via the Conditional Randomization Test
- 结合条件随机化检验与TabPFN，实现特征层面的统计推断
- 在非线性相关场景下仍能给出有限样本有效的p值
- 无需重训练模型或假设分布，适合数据科学家快速验证
现代机器学习模型表达能力强但难以进行统计分析。尽管黑箱预测器表现优异，却很少提供有效假设检验或p值来判断单个特征是否包含目标变量信息。本文提出一种实用方法，将条件随机化检验（CRT）与TabPFN——一种用于表格数据的概率基础模型相结合。该方法在非线性与特征相关场景下，无需模型重训练或参数假设，即可获得条件特征相关性的有限样本有效p值，实现特征级别的可靠统计推断。
原文摘要 · Abstract (English)
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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