We propose a novel framework that leverages LLMs for full causal graph discovery. While previous LLM-based methods have used a pairwise query approach, this requires a quadratic number of queries which quickly becomes impractical for larger causal graphs. In contrast, the proposed framework uses a breadth-first search (BFS) approach which allows it to use only a linear number of queries. We also show that the proposed method can easily incorporate observational data when available, to improve performance. In addition to being more time and data-efficient, the proposed framework achieves state-of-the-art results on real-world causal graphs of varying sizes. The results demonstrate the effectiveness and efficiency of the proposed method in discovering causal relationships, showcasing its potential for broad applicability in causal graph discovery tasks across different domains.

翻译：我们提出了一种利用大型语言模型进行全因果图发现的新型框架。以往基于LLM的方法采用两两查询策略，这需要二次数量的查询，对于较大的因果图而言很快变得不切实际。相比之下，本框架采用广度优先搜索（BFS）方法，仅需线性数量的查询即可完成。我们还证明，该提出的方法在可用时可轻松融入观测数据以提升性能。除了在时间和数据效率上更具优势外，该框架在多种规模的真实因果图上取得了最先进的成果。实验结果展示了该方法在发现因果关系方面的有效性和高效性，彰显了其在跨领域因果图发现任务中的广泛适用潜力。