  Large Language Models (LLMs) exhibit exceptional abilities for causal
analysis between concepts in numerous societally impactful domains, including
medicine, science, and law. Recent research on LLM performance in various
causal discovery and inference tasks has given rise to a new ladder in the
classical three-stage framework of causality. In this paper, we advance the
current research of LLM-driven causal discovery by proposing a novel framework
that combines knowledge-based LLM causal analysis with data-driven causal
structure learning. To make LLM more than a query tool and to leverage its
power in discovering natural and new laws of causality, we integrate the
valuable LLM expertise on existing causal mechanisms into statistical analysis
of objective data to build a novel and practical baseline for causal structure
learning.
  We introduce a universal set of prompts designed to extract causal graphs
from given variables and assess the influence of LLM prior causality on
recovering causal structures from data. We demonstrate the significant
enhancement of LLM expertise on the quality of recovered causal structures from
data, while also identifying critical challenges and issues, along with
potential approaches to address them. As a pioneering study, this paper aims to
emphasize the new frontier that LLMs are opening for classical causal discovery
and inference, and to encourage the widespread adoption of LLM capabilities in
data-driven causal analysis.
