  Large Language Models (LLMs) have demonstrated significant potential in
causal discovery tasks by utilizing their vast expert knowledge from extensive
text corpora. However, the multi-agent capabilities of LLMs in causal discovery
remain underexplored. This paper introduces a general framework to investigate
this potential. The first is the Meta Agents Model, which relies exclusively on
reasoning and discussions among LLM agents to conduct causal discovery. The
second is the Coding Agents Model, which leverages the agents' ability to plan,
write, and execute code, utilizing advanced statistical libraries for causal
discovery. The third is the Hybrid Model, which integrates both the Meta Agents
Model and CodingAgents Model approaches, combining the statistical analysis and
reasoning skills of multiple agents. Our proposed framework shows promising
results by effectively utilizing LLMs expert knowledge, reasoning capabilities,
multi-agent cooperation, and statistical causal methods. By exploring the
multi-agent potential of LLMs, we aim to establish a foundation for further
research in utilizing LLMs multi-agent for solving causal-related problems.
