{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T03:12:34Z","timestamp":1763694754032,"version":"3.45.0"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Ltd","issue":"16","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2025,12,30]]},"abstract":"<jats:p>To accurately detect structural changes driven by shifts in underlying causal mechanisms, we propose CaSCo (Causal Structure Comparison), a novel framework that integrates causal inference with deep representation learning. CaSCo first leverages a differentiable causal structure learning method based on NOTEARS to construct causal graphs from observational data, uncovering the latent causal dependencies among variables. To capture subtle local structural variations and nonlinear patterns, a graph neural network (GNN) using Graph Isomorphism Network (GIN) architecture is employed to encode these causal graphs into expressive embeddings. By comparing the learned causal representations across different time windows or data segments, CaSCo introduces a structural change scoring mechanism that identifies abrupt or gradual changes in the system\u2019s causal dynamics. The framework is general and scalable, making it applicable to tasks such as time-series monitoring, anomaly detection, and system migration analysis. Extensive experiments on both synthetic and real-world datasets demonstrate that CaSCo outperforms existing statistical and deep learning-based approaches in detecting structural changes, particularly under nonstationary environments and high-dimensional conditions.<\/jats:p>","DOI":"10.1142\/s0218001425520263","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T04:28:46Z","timestamp":1758947326000},"source":"Crossref","is-referenced-by-count":0,"title":["A Structural Change Detection Method Integrating Causal Inference and Deep Learning"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7780-2646","authenticated-orcid":false,"given":"Tong","family":"Liu","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong 999007, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5476-2228","authenticated-orcid":false,"given":"Hualin","family":"Liu","sequence":"additional","affiliation":[{"name":"People\u2019s Public Security University of China, Beijing 100038, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"S0218001425520263BIB001","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.2012.00819.x"},{"key":"S0218001425520263BIB003","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.1910340409"},{"key":"S0218001425520263BIB004","doi-asserted-by":"publisher","DOI":"10.1002\/qub2.26"},{"key":"S0218001425520263BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414770"},{"key":"S0218001425520263BIB006","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw042"},{"key":"S0218001425520263BIB007","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98131-4_3"},{"key":"S0218001425520263BIB008","doi-asserted-by":"publisher","DOI":"10.2307\/1912559"},{"key":"S0218001425520263BIB009","doi-asserted-by":"publisher","DOI":"10.1002\/cem.800"},{"key":"S0218001425520263BIB010","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"S0218001425520263BIB011","series-title":"Springer Series in Statistics","volume-title":"Principal Component Analysis","author":"Jolliffee I.","year":"2002"},{"key":"S0218001425520263BIB012","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2012.737745"},{"key":"S0218001425520263BIB013","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"S0218001425520263BIB014","doi-asserted-by":"publisher","DOI":"10.1038\/ng1760"},{"key":"S0218001425520263BIB016","doi-asserted-by":"publisher","DOI":"10.2307\/2333009"},{"volume-title":"Elements of Causal Inference: Foundations and Learning Algorithms","year":"2017","author":"Peters J.","key":"S0218001425520263BIB017"},{"key":"S0218001425520263BIB018","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aau4996"},{"key":"S0218001425520263BIB019","doi-asserted-by":"publisher","DOI":"10.1126\/science.1105809"},{"key":"S0218001425520263BIB020","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-6261.1970.tb00516.x"},{"key":"S0218001425520263BIB022","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani A.","year":"2017"},{"key":"S0218001425520263BIB023","doi-asserted-by":"publisher","DOI":"10.1016\/j.snb.2012.01.074"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001425520263","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:56:19Z","timestamp":1763686579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218001425520263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,30]]},"references-count":20,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2025,12,30]]}},"alternative-id":["10.1142\/S0218001425520263"],"URL":"https:\/\/doi.org\/10.1142\/s0218001425520263","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"type":"print","value":"0218-0014"},{"type":"electronic","value":"1793-6381"}],"subject":[],"published":{"date-parts":[[2025,10,30]]},"article-number":"2552026"}}