Integrating Large Language Model for Improved Causal Discovery

Understand

Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios.

• Domain-specific causal discovery usually relies on expert validation or prior analysis to improve the reliability of recovered causality, which is yet limited by the scarcity of expert resources.
• Recently, Large Language Models (LLM) have been used for causal analysis across various domain-specific scenarios, suggesting its potential as autonomous expert roles in guiding data-based structure learning.
• However, integrating LLMs into causal discovery faces challenges due to inaccuracies in LLM-based reasoning on revealing the actual causal structure.