In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is widely accepted as significant for creating consistent meaningful causal models, despite the recognized challenges in systematic acquisition of the background knowledge. To overcome these challenges, this paper proposes a novel methodology for causal inference, in which SCD methods and knowledge based causal inference (KBCI) with a large language model (LLM) are synthesized through "statistical causal prompting (SCP)" for LLMs and prior knowledge augmentation for SCD. Experiments have revealed that GPT-4 can cause the output of the LLM-KBCI and the SCD result with prior knowledge from LLM-KBCI to approach the ground truth, and that the SCD result can be further improved, if GPT-4 undergoes SCP. Furthermore, it has been clarified that an LLM can improve SCD with its background knowledge, even if the LLM does not contain information on the dataset. The proposed approach can thus address challenges such as dataset biases and limitations, illustrating the potential of LLMs to improve data-driven causal inference across diverse scientific domains.

翻译：在实际的统计因果发现中，将领域专家知识以约束形式嵌入算法，被广泛认为对于生成一致且有意义的因果模型具有重要意义，尽管系统性地获取背景知识仍面临公认的挑战。为克服这些挑战，本文提出了一种新的因果推断方法论，通过针对大型语言模型的“统计因果提示”以及针对统计因果发现的先验知识增强，将统计因果发现方法与基于知识的大型语言模型因果推断相融合。实验表明，GPT-4能够使基于知识的大型语言模型因果推断的输出，以及从该推断结果获取先验知识后的统计因果发现结果趋近于真实情况；并且，若对GPT-4进行统计因果提示，则统计因果发现结果可进一步改善。此外，研究阐明，即使大型语言模型未包含数据集的相关信息，其背景知识也能改进统计因果发现。因此，所提出的方法能够应对数据集偏差和局限性等挑战，展示了大型语言模型在跨不同科学领域改进数据驱动因果推断方面的潜力。