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.