Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models presents an innovative approach to recovering causal graphs through RAG-based large language models. The methodology leverages extensive knowledge from various scientific literature to deduce causal relationships, providing a unique way of extracting and analyzing information systematically. This approach emphasizes identifying potential associations between factors and then aggregating them to construct a causal graph.

Essential points:

• Utilizes RAG-based LLMs to derive causality from aggregated literature.
• Provides a system for labeling and aggregating associational relationships.
• Demonstrates high-quality causal graph construction on well-known datasets.

The paper holds significant value as it offers a novel perspective on causal inference, utilizing retrievable scientific knowledge to form causal graphs, which is especially important in domains requiring complex relational understanding, like healthcare and economics.