AI 中文总结
研究针对条件随机化测试中估计误差、高维性及样本量问题，提出CRT*框架，用平滑残差自举结合自适应数据融合增强CI测试，理论证明其有效性，模拟和数据分析显示该框架能在异构场景中提高功效并控制I型错误。
AI 中文摘要
条件随机化测试（CRT）为条件独立性（CI）测试提供了一种有原则的方法，在已知真实条件分布时保证精确的I型错误控制。但实际中该分布需估计，估计误差会增大I型错误，高维性和有限样本量会降低检验功效。外部和未标记数据虽能改善CI测试，但简单整合会损害I型错误控制且无法提高功效。我们提出CRT*，一个能在异构场景中稳健整合外部和未标记数据集以增强CI测试的新框架。CRT*采用带迁移学习的平滑残差自举（SRB）进行条件分布估计，并通过检验统计量的最优凸组合进行自适应数据融合。理论上证明基于SRB的估计器在期望总变差距离上收敛到真实条件分布。此外，即使在高维情况下，CRT*也能保持有效的I型错误控制，且比无外部数据的标准CRT具有更高的功效。模拟和RNA测序乳腺癌数据分析表明，CRT*在异构设置中能大幅提高功效并保持I型错误控制。
英文摘要
The conditional randomization test (CRT) provides a principled approach to conditional independence (CI) testing, guaranteeing exact type-I error control when the true conditional distribution is known. In practice, however, this distribution must be estimated, and estimation errors can inflate type-I errors, while high dimensionality and limited sample sizes can reduce power. Although external and unlabeled data offer the potential to improve CI testing, naive integration that ignores distributional heterogeneity can compromise type-I error control and fail to enhance power. We propose \textbf{CRT*}, a novel framework that robustly integrates external and unlabeled datasets to enhance CI testing in heterogeneous scenarios. CRT* employs smooth residual-bootstrap (SRB) with transfer learning for conditional distribution estimation, combined with adaptive data fusion via an optimal convex combination of test statistics. We theoretically establish that the SRB-based estimator converges to the true conditional distribution in expected total variation distance. Furthermore, even in high-dimensional regimes, CRT* maintains valid type-I error control and achieves strictly higher power than standard CRT without external data. Simulations and RNA-seq breast cancer data analyses demonstrate that CRT* substantially improves power while maintaining type-I error control in heterogeneous settings.
