  Causal graph recovery is traditionally done using statistical
estimation-based methods or based on individual's knowledge about variables of
interests. They often suffer from data collection biases and limitations of
individuals' knowledge. The advance of large language models (LLMs) provides
opportunities to address these problems. We propose a novel method that
leverages LLMs to deduce causal relationships in general causal graph recovery
tasks. This method leverages knowledge compressed in LLMs and knowledge LLMs
extracted from scientific publication database as well as experiment data about
factors of interest to achieve this goal. Our method gives a prompting strategy
to extract associational relationships among those factors and a mechanism to
perform causality verification for these associations. Comparing to other
LLM-based methods that directly instruct LLMs to do the highly complex causal
reasoning, our method shows clear advantage on causal graph quality on
benchmark datasets. More importantly, as causality among some factors may
change as new research results emerge, our method show sensitivity to new
evidence in the literature and can provide useful information for updating
causal graphs accordingly.
