Large Language Models for Causal Discovery: Current Landscape and Future Directions
This work provides a foundational survey for researchers in AI and causality, identifying research gaps and future directions, but it is incremental as it reviews existing approaches rather than introducing new methods.
This survey examines how large language models (LLMs) are transforming causal discovery (CD) by integrating them for tasks like direct causal extraction from text and refining causal structures, highlighting both their potential and current limitations as imperfect expert systems.
Causal discovery (CD) and Large Language Models (LLMs) have emerged as transformative fields in artificial intelligence that have evolved largely independently. While CD specializes in uncovering cause-effect relationships from data, and LLMs excel at natural language processing and generation, their integration presents unique opportunities for advancing causal understanding. This survey examines how LLMs are transforming CD across three key dimensions: direct causal extraction from text, integration of domain knowledge into statistical methods, and refinement of causal structures. We systematically analyze approaches that leverage LLMs for CD tasks, highlighting their innovative use of metadata and natural language for causal inference. Our analysis reveals both LLMs' potential to enhance traditional CD methods and their current limitations as imperfect expert systems. We identify key research gaps, outline evaluation frameworks and benchmarks for LLM-based causal discovery, and advocate future research efforts for leveraging LLMs in causality research. As the first comprehensive examination of the synergy between LLMs and CD, this work lays the groundwork for future advances in the field.