  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.
