Bridging Causal Discovery and Large Language Models: A Comprehensive
  Survey of Integrative Approaches and Future Directions
Guangya Wan Yuqi Wu Mengxuan Hu Zhixuan Chu Sheng Li
Causal discovery (CD) and Large Language Models (LLMs) represent two emerging
fields of study with significant implications for artificial intelligence.
Despite their distinct origins, CD focuses on uncovering cause-effect
relationships from data, and LLMs on processing and generating humanlike text,
the convergence of these domains offers novel insights and methodologies for
understanding complex systems. This paper presents a comprehensive survey of
the integration of LLMs, such as GPT4, into CD tasks. We systematically review
and compare existing approaches that leverage LLMs for various CD tasks and
highlight their innovative use of metadata and natural language to infer causal
structures. Our analysis reveals the strengths and potential of LLMs in both
enhancing traditional CD methods and as an imperfect expert, alongside the
challenges and limitations inherent in current practices. Furthermore, we
identify gaps in the literature and propose future research directions aimed at
harnessing the full potential of LLMs in causality research. To our knowledge,
this is the first survey to offer a unified and detailed examination of the
synergy between LLMs and CD, setting the stage for future advancements in the
field.