This paper proposes a novel methodology for statistical causal discovery (SCD) that integrates large language models (LLMs) with traditional SCD methods through "statistical causal prompting (SCP)" and prior knowledge augmentation. The approach combines SCD methods with knowledge-based causal inference (KBCI) using LLMs, enabling more accurate causal modeling by incorporating domain expert knowledge. The method involves two main steps: first, performing SCD on a dataset without prior knowledge, and then using the results of this SCD to generate and integrate domain knowledge into the LLM for improved causal inference. The proposed method uses GPT-4 to generate domain knowledge and then applies this knowledge to enhance SCD results. Experiments show that GPT-4 can improve the accuracy of SCD results when prompted with statistical causal information, and that the SCD results can be further improved when GPT-4 undergoes SCP. The method was tested on several benchmark datasets and an unpublished real-world dataset, demonstrating its effectiveness in improving SCD results even when the dataset is not part of the LLM's training data. The study highlights the potential of LLMs to enhance data-driven causal inference across various scientific domains by providing domain knowledge that can be integrated into SCD algorithms. The method also addresses challenges such as dataset biases and limitations by leveraging the statistical properties of the data and the domain knowledge provided by LLMs. The results show that the proposed approach can lead to more statistically valid causal models, especially when the dataset contains biases or is not fully known to the LLM. The paper also discusses the limitations of the approach, including the reliance on GPT-4 and the need for further research on optimal LLMs and techniques for enhancing SCD results. The broader impact of the study is highlighted, emphasizing the potential of integrating LLMs with SCD methods to improve causal inference in fields such as healthcare, economics, and environmental science, while also considering the ethical implications of using LLMs in such contexts.This paper proposes a novel methodology for statistical causal discovery (SCD) that integrates large language models (LLMs) with traditional SCD methods through "statistical causal prompting (SCP)" and prior knowledge augmentation. The approach combines SCD methods with knowledge-based causal inference (KBCI) using LLMs, enabling more accurate causal modeling by incorporating domain expert knowledge. The method involves two main steps: first, performing SCD on a dataset without prior knowledge, and then using the results of this SCD to generate and integrate domain knowledge into the LLM for improved causal inference. The proposed method uses GPT-4 to generate domain knowledge and then applies this knowledge to enhance SCD results. Experiments show that GPT-4 can improve the accuracy of SCD results when prompted with statistical causal information, and that the SCD results can be further improved when GPT-4 undergoes SCP. The method was tested on several benchmark datasets and an unpublished real-world dataset, demonstrating its effectiveness in improving SCD results even when the dataset is not part of the LLM's training data. The study highlights the potential of LLMs to enhance data-driven causal inference across various scientific domains by providing domain knowledge that can be integrated into SCD algorithms. The method also addresses challenges such as dataset biases and limitations by leveraging the statistical properties of the data and the domain knowledge provided by LLMs. The results show that the proposed approach can lead to more statistically valid causal models, especially when the dataset contains biases or is not fully known to the LLM. The paper also discusses the limitations of the approach, including the reliance on GPT-4 and the need for further research on optimal LLMs and techniques for enhancing SCD results. The broader impact of the study is highlighted, emphasizing the potential of integrating LLMs with SCD methods to improve causal inference in fields such as healthcare, economics, and environmental science, while also considering the ethical implications of using LLMs in such contexts.