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：我们提出一种利用大语言模型进行完整因果图发现的新型框架。尽管现有基于大语言模型的方法采用成对查询策略，但这种方式需要与变量数量呈二次增长的查询次数，在处理较大规模因果图时很快变得不可行。相比之下，本框架采用广度优先搜索方法，仅需线性次查询即可完成。研究进一步表明，所提方法可便捷地整合观测数据（若可获得）以提升性能。除具有更高时间效率和数据效率外，该框架在不同规模的真实世界因果图上均取得了最先进水平。实验结果证实了所提方法在发现因果关系方面的有效性与高效性，展示了其在跨领域因果图发现任务中的广泛应用潜力。