---
title: "TCCD: Tree-guided Continuous Causal Discovery via Collaborative MCTS-Parameter Optimization"
authors:
  - "Jingjin Liu"
  - "Yingkai Xiao"
  - "Hankz Hankui Zhuo"
  - "Wushao Wen"
doi: "10.24963/ijcai.2025/1030"
url: "https://ijcai.org/proceedings/2025/1030"
pdf: "https://ijcai.org/proceedings/2025/1030.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "9268-9276"
track: "AI4Tech: AI Enabling Technologies"
keywords:
  - "Domain-specific AI4Tech: AI4Telecom"
  - "Advanced AI4Tech: Data-driven AI4Tech"
  - "Advanced AI4Tech: Deep AI4Tech"
---

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

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

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 9268-9276. DOI: [10.24963/ijcai.2025/1030](https://doi.org/10.24963/ijcai.2025/1030)

## 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.