{"type":"ScholarlyArticle","title":"Learning Kernelized Hypothesis for Hidden Confounder Detection","authors":[{"name":"Yikai Chen","slug":"yikai-chen","given":"Yikai","family":"Chen","affiliation":""},{"name":"Haotian Wang","slug":"haotian-wang","given":"Haotian","family":"Wang","affiliation":""},{"name":"Yunxin Mao","slug":"yunxin-mao","given":"Yunxin","family":"Mao","affiliation":""},{"name":"Xinpeng Lv","slug":"xinpeng-lv","given":"Xinpeng","family":"Lv","affiliation":""},{"name":"Shaowu Yang","slug":"shaowu-yang","given":"Shaowu","family":"Yang","affiliation":""},{"name":"Kun Kuang","slug":"kun-kuang","given":"Kun","family":"Kuang","affiliation":""},{"name":"Xinwang Liu","slug":"xinwang-liu","given":"Xinwang","family":"Liu","affiliation":""},{"name":"Wenjing Yang","slug":"wenjing-yang","given":"Wenjing","family":"Yang","affiliation":""}],"abstract":"Detecting hidden confounding is crucial for reliable causal analysis from observational data, directly determining which downstream causal inference method to be deployed. Inspired by the theory of higher-order regression, recent sample-efficient hypothesis testing strategies overcome the restrictive requirement of multiple heterogeneous data environment. Despite their progress on single-environment confounder detection, such methods suffer from intrinsic flaws that the structural functions of the causal models should be specified in prior (linear or specific kernel functions). By contrast, real-world data acquisition exhibits diverse, unknown forms of structural functions, imposing an important but challenging gap between theories of higher-order regressions to practical confounder detection. In this paper, we contribute a Bi-level Kernel Confounder Detection (BiKCD) framework by learning adaptive kernelized structural space of structural functions. Subsequently, our BiKCD constructs hypothesis testing by comparing coefficients from the higher-order regression and the classical ordinary least squares in learned kernelized space. Finally, the hypothesis is calibrated to ensure valid inference under adaptivity. Theoretically, we establish an oracle-type risk bound for the selected structural space over a candidate kernel family, with the Type-I error control for the downstream test. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed BiKCD.","doi":"10.24963/ijcai.2026/467","url":"https://ijcai.org/proceedings/2026/467","pdf":"https://ijcai.org/proceedings/2026/0467.pdf","markdown":"https://ijcai.org/proceedings/2026/467.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/467","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":467,"pages":{"first":4197,"last":4205},"keywords":["Machine Learning: Causality"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}