Causal discovery from observational data is pivotal for deciphering complex relationships. Causal Structure Learning (CSL), which focuses on deriving causal Directed Acyclic Graphs (DAGs) from data, faces challenges due to vast DAG spaces and data sparsity. The integration of Large Language Models (LLMs), recognized for their causal reasoning capabilities, offers a promising direction to enhance CSL by infusing it with knowledge-based causal inferences. However, existing approaches utilizing LLMs for CSL have encountered issues, including unreliable constraints from imperfect LLM inferences and the computational intensity of full pairwise variable analyses. In response, we introduce the Iterative LLM Supervised CSL (ILS-CSL) framework. ILS-CSL innovatively integrates LLM-based causal inference with CSL in an iterative process, refining the causal DAG using feedback from LLMs. This method not only utilizes LLM resources more efficiently but also generates more robust and high-quality structural constraints compared to previous methodologies. Our comprehensive evaluation across eight real-world datasets demonstrates ILS-CSL's superior performance, setting a new standard in CSL efficacy and showcasing its potential to significantly advance the field of causal discovery. The codes are available at \url{https://github.com/tyMadara/ILS-CSL}.

翻译：从观测数据中发现因果结构对于理解复杂关系至关重要。因果结构学习旨在从数据中推导出因果有向无环图，但面临着巨大的有向无环图空间和数据稀疏性的挑战。大型语言模型因其因果推理能力而被广泛认可，将其引入因果结构学习，通过注入基于知识的因果推理来增强其性能，展现出前景广阔的发展方向。然而，现有利用大型语言模型进行因果结构学习的方法存在一些问题，包括来自不完善的大型语言模型推理的不可靠约束，以及全对变量分析的计算强度过大。为此，我们提出了迭代式大型语言模型监督的因果结构学习框架。该框架创新性地将基于大型语言模型的因果推理与因果结构学习迭代集成，利用大型语言模型的反馈来优化因果有向无环图。与以往方法相比，该方法不仅更高效地利用大型语言模型资源，还能生成更稳健、更高质量的约束条件。我们在八个真实世界数据集上的综合评估表明，ILS-CSL性能卓越，树立了因果结构学习效能的新标杆，并展示了其推动因果发现领域发展的巨大潜力。代码可在\url{https://github.com/tyMadara/ILS-CSL}获取。