Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

The Causal Ensemble Agent (CEA) framework was introduced for hierarchical causal discovery, integrating insights from various causal discovery algorithms and employing a Large Language Model (LLM) as a meta-referee for expert reweighting. CEA utilizes linear opinion pooling to aggregate structural insights and dynamically adjusts expert contributions based on confidence levels, resulting in improved causal graph accuracy. This approach demonstrates significant performance enhancements across multiple datasets, underscoring the potential of LLMs in refining causal inference methodologies for practitioners.