  Recent advances in artificial intelligence have seen Large Language Models
(LLMs) demonstrate notable proficiency in causal discovery tasks. This study
explores the factors influencing the performance of LLMs in causal discovery
tasks. Utilizing open-source LLMs, we examine how the frequency of causal
relations within their pre-training corpora affects their ability to accurately
respond to causal discovery queries. Our findings reveal that a higher
frequency of causal mentions correlates with better model performance,
suggesting that extensive exposure to causal information during training
enhances the models' causal discovery capabilities. Additionally, we
investigate the impact of context on the validity of causal relations. Our
results indicate that LLMs might exhibit divergent predictions for identical
causal relations when presented in different contexts. This paper provides the
first comprehensive analysis of how different factors contribute to LLM
performance in causal discovery tasks.
