{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:58Z","timestamp":1758672898095,"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>Learning causal relationships in directed acyclic graphs (DAGs) from multi-type event sequences is a challenging task, especially in large-scale telecommunication networks. Existing methods struggle with the exponentially growing search space and lack global exploration. Gradient-based approaches are limited by their reliance on local information and often fail to generalize. To address these issues, we propose TCCD, a framework that combines Monte Carlo Tree Search (MCTS) with continuous gradient optimization. TCCD balances global exploration and local optimization, overcoming the shortcomings of purely gradient-based methods and enhancing generalization. By unifying various causal structure learning approaches, TCCD offers a scalable and efficient solution for causal inference in complex networks. Extensive experiments validate its superior performance on both synthetic and real-world datasets. Code and Appendix are available at https:\/\/github.com\/jzephyrl\/TCCD.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1030","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"9268-9276","source":"Crossref","is-referenced-by-count":0,"title":["TCCD: Tree-guided Continuous Causal Discovery via Collaborative MCTS-Parameter Optimization"],"prefix":"10.24963","author":[{"given":"Jingjin","family":"Liu","sequence":"first","affiliation":[{"name":"Sun Yat-sen University"}]},{"given":"Yingkai","family":"Xiao","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University"}]},{"given":"Hankz Hankui","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Wushao","family":"Wen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen Unviersity"}]}],"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":"2025-09-23T11:35:55Z","timestamp":1758627355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1030"}},"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\/1030","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}