Proceedings

TCCD: Tree-guided Continuous Causal Discovery via Collaborative MCTS-Parameter Optimization

Jingjin Liu, Yingkai Xiao, Hankz Hankui Zhuo, Wushao Wen

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI4Tech: AI Enabling Technologies. Pages 9268-9276.

https://doi.org/10.24963/ijcai.2025/1030

Keywords

  • Domain-specific AI4Tech: AI4Telecom
  • Advanced AI4Tech: Data-driven AI4Tech
  • Advanced AI4Tech: Deep AI4Tech

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.

Jingjin Liu, Yingkai Xiao, Hankz Hankui Zhuo, and Wushao Wen. 2025. TCCD: Tree-guided Continuous Causal Discovery via Collaborative MCTS-Parameter Optimization. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9268–9276. https://doi.org/10.24963/ijcai.2025/1030
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