{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:50Z","timestamp":1758672890388,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Testing conditional independence is a critical task, particularly in causal discovery and learning in Bayesian networks. However, in many real-world scenarios, variables are often measured with errors, such as those introduced by insufficient measurement accuracy, complicating the testing process. This paper focuses on testing conditional independence in the linear non-Gaussian measurement error model, under the condition that measurement error noise follows a Gaussian distribution. By leveraging high-order cumulants, we derive rank constraints on the cumulant matrix and establish their role in effectively assessing conditional independence, even in the presence of measurement errors. Based on these theoretical results, we leverage the rank constraints of the cumulant matrix as a tool for conditional independence testing and incorporate it into the PC algorithm, resulting in the PC-ME algorithm \u2014 a method designed to learn causal structures from observed data while accounting for measurement errors. Experimental results demonstrate that the proposed method outperforms existing approaches, particularly in cases other methods encounter difficulties.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1013","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9112-9120","source":"Crossref","is-referenced-by-count":0,"title":["Conditional Independent Test in the Presence of Measurement Error with Causal Structure Learning"],"prefix":"10.24963","author":[{"given":"Hongbin","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangdong University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kezhou","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nankai","family":"Lin","sequence":"additional","affiliation":[{"name":"Guangdong University of Foreign Studies"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aimin","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[{"name":"Shantou University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengming","family":"Chen","sequence":"additional","affiliation":[{"name":"Shantou University"},{"name":"Guangdong University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:51Z","timestamp":1758627351000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1013"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1013","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}