This research introduces a novel multi-agent framework that harnesses large language models to identify causal relationships between variables, combining statistical analysis with contextual metadata.

• Integrates both structured data and metadata for more comprehensive causal discovery
• Employs a multi-agent system where LLMs collaborate to uncover complex causal links
• Demonstrates potential to enhance traditional causal discovery methods through language model capabilities
• Shows particular promise for medical applications where understanding causes is critical

For medical professionals, this approach offers a powerful new tool to identify causal factors in diseases, improve diagnostic accuracy, and develop more targeted treatment strategies by incorporating previously overlooked contextual information.

Multi-Agent Causal Discovery Using Large Language Models