We introduce local conditional hypotheses that express how the relation between explanatory variables and outcomes changes across different contexts, described by covariates. By expanding upon the model-X knockoff filter, we show how to adaptively discover these local associations, all while controlling the false discovery rate. Our enhanced inferences can help explain sample heterogeneity and uncover interactions, making better use of the capabilities offered by modern machine learning models. Specifically, our method is able to leverage any model for the identification of data-driven hypotheses pertaining to different contexts. Then, it rigorously test these hypotheses without succumbing to selection bias. Importantly, our approach is efficient and does not require sample splitting. We demonstrate the effectiveness of our method through numerical experiments and by studying the genetic architecture of Waist-Hip-Ratio across different sexes in the UKBiobank.

翻译：本文提出了局部条件假设，用以描述解释变量与结果之间的关系如何随协变量所定义的不同情境而变化。通过扩展模型-X knockoff滤波器，我们展示了如何在控制错误发现率的前提下自适应地发现这些局部关联。我们增强的推断能力有助于解释样本异质性并揭示交互作用，从而更好地利用现代机器学习模型所提供的功能。具体而言，我们的方法能够利用任何模型来识别与不同情境相关的数据驱动假设，随后严格检验这些假设而不会陷入选择偏差。重要的是，本方法高效且无需样本分割。我们通过数值实验以及研究UKBiobank中不同性别下腰臀比的遗传结构，验证了该方法的有效性。