Semi-knockoffs: a model-agnostic conditional independence testing method with finite-sample guarantees
半敲诈：一种具有有限样本保证的模型无关条件独立性检验方法
Conditional independence testing (CIT) is essential for reliable scientific discovery. It prevents spurious findings and enables controlled feature selection. Recent CIT methods have used machine learning (ML) models as surrogates of the underlying distribution. However, model-agnostic approaches require a train-test split, which reduces statistical power. We introduce Semi-knockoffs, a CIT method that can accommodate any pre-trained model, avoids this split, and provides…