Searching for Local Associations while Controlling the False Discovery Rate

• 成果类型: Article; Early Access
• 署名作者: Gablenz, Paula; Sesia, Matteo; Sun, Tianshu; Sabatti, Chiara
• 署名单位: Stanford University; University of Southern California; University of Southern California; Stanford University; Stanford Medicine; Stanford University; Stanford Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2616856
• 发表日期: 2026-03-23
• 关键词: conditional independence; False Discovery Rate; heterogeneous effects; interactions; knockoffs; Selective Inference; subgroup analysis; inference; MODEL

摘要

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. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.