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，利用大型语言模型实现完整的因果图发现。先前基于LLM的方法采用成对查询方式，这需要平方级数量的查询，对于大型因果图而言很快变得不切实际。相比之下，所提出的框架采用广度优先搜索方法，仅需线性数量的查询。我们还表明，所提方法可以轻松整合可用的观测数据以提升性能。除了更加省时和数据高效外，该框架在多个不同规模的真实因果图上取得了最先进的成果。实验结果证明了所提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应用潜力。