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：我们提出了一种新颖框架，利用大语言模型（LLMs）实现完整的因果图发现。以往的基于LLM的方法采用成对查询策略，需要进行二次数量的查询，这在处理较大规模因果图时迅速变得不可行。相比之下，所提出的框架采用广度优先搜索（BFS）方法，仅需线性数量的查询。我们还证明，该方法能够轻松融入可用的观测数据以提升性能。除在时间和数据效率方面更具优势外，该框架在不同规模的真实因果图上均取得了最先进的结果。实验结果证明了该方法在发现因果关系方面的有效性和高效性，展示了其在跨领域因果图发现任务中的广泛适用潜力。