Efficient Causal Graph Discovery Using Large Language Models

Researchers propose a new framework using Large Language Models for causal graph discovery that requires only linear queries instead of quadratic, making it more efficient for larger datasets. The method uses breadth-first search and can incorporate observational data, achieving state-of-the-art results on real-world causal graphs.