{"type":"ScholarlyArticle","title":"TCCD: Tree-guided Continuous Causal Discovery via Collaborative MCTS-Parameter Optimization","authors":[{"name":"Jingjin Liu","slug":"jingjin-liu","given":"Jingjin","family":"Liu","affiliation":""},{"name":"Yingkai Xiao","slug":"yingkai-xiao","given":"Yingkai","family":"Xiao","affiliation":""},{"name":"Hankz Hankui Zhuo","slug":"hankz-hankui-zhuo","given":"Hankz Hankui","family":"Zhuo","affiliation":""},{"name":"Wushao Wen","slug":"wushao-wen","given":"Wushao","family":"Wen","affiliation":""}],"abstract":"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.","doi":"10.24963/ijcai.2025/1030","url":"https://ijcai.org/proceedings/2025/1030","pdf":"https://ijcai.org/proceedings/2025/1030.pdf","markdown":"https://ijcai.org/proceedings/2025/1030.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1030","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1030,"pages":{"first":9268,"last":9276},"keywords":["Domain-specific AI4Tech: AI4Telecom","Advanced AI4Tech: Data-driven AI4Tech","Advanced AI4Tech: Deep AI4Tech"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}