{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:02:17Z","timestamp":1773802937104,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"24","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous environments, limiting applicability to nonlinear single-environment settings. To bridge this gap, we propose Kernel Regression Confounder Detection (KRCD), a novel method for detecting unobserved confounding in nonlinear observational data under single-environment conditions. KRCD leverages reproducing kernel Hilbert spaces to model complex dependencies. By comparing standard and higher-order kernel regressions, we derive a test statistic whose significant deviation from zero indicates unobserved confounding. Theoretically, we prove two key results: First, in infinite samples, regression coefficients coincide if and only if no unobserved confounders exist. Second, finite-sample differences converge to zero-mean Gaussian distributions with tractable variance. Extensive experiments on synthetic benchmarks and the Twins dataset demonstrate that KRCD not only outperforms existing baselines but also achieves superior computational efficiency.<\/jats:p>","DOI":"10.1609\/aaai.v40i24.39127","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:11:02Z","timestamp":1773796262000},"page":"20381-20389","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Unobserved Confounders: A Kernelized Regression Approach"],"prefix":"10.1609","volume":"40","author":[{"given":"Yikai","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yunxin","family":"Mao","sequence":"additional","affiliation":[]},{"given":"Chunyuan","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Zou","sequence":"additional","affiliation":[]},{"given":"Shanzhi","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Shixuan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Wenjing","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Kuang","sequence":"additional","affiliation":[]},{"given":"Haotian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39127\/43089","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/39127\/43089","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:11:02Z","timestamp":1773796262000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/39127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i24.39127","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}