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Large Language Models (LLMs) exhibit exceptional abilities for causalanalysis between concepts in numerous societally impactful domains, includingmedicine, science, and law. Recent research on LLM performance in variouscausal discovery and inference tasks has given rise to a new ladder in theclassical three-stage framework of causality. In this paper, we advance thecurrent research of LLM-driven causal discovery by proposing a novel frameworkthat combines knowledge-based LLM causal analysis with data-driven causalstructure learning. To make LLM more than a query tool and to leverage itspower in discovering natural and new laws of causality, we integrate thevaluable LLM expertise on existing causal mechanisms into statistical analysisof objective data to build a novel and practical baseline for causal structurelearning.  We introduce a universal set of prompts designed to extract causal graphsfrom given variables and assess the influence of LLM prior causality onrecovering causal structures from data. We demonstrate the significantenhancement of LLM expertise on the quality of recovered causal structures fromdata, while also identifying critical challenges and issues, along withpotential approaches to address them. As a pioneering study, this paper aims toemphasize the new frontier that LLMs are opening for classical causal discoveryand inference, and to encourage the widespread adoption of LLM capabilities indata-driven causal analysis.

From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data