To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observational data. However, obtaining a complete and accurate causal graph poses a formidable challenge, recognized as an NP-hard problem. Recently, the advent of Large Language Models (LLMs) has ushered in a new era, indicating their emergent capabilities and widespread applicability in facilitating causal reasoning across diverse domains, such as medicine, finance, and science. The expansive knowledge base of LLMs holds the potential to elevate the field of causal reasoning by offering interpretability, making inferences, generalizability, and uncovering novel causal structures. In this paper, we introduce a new framework, named Autonomous LLM-Augmented Causal Discovery Framework (ALCM), to synergize data-driven causal discovery algorithms and LLMs, automating the generation of a more resilient, accurate, and explicable causal graph. The ALCM consists of three integral components: causal structure learning, causal wrapper, and LLM-driven causal refiner. These components autonomously collaborate within a dynamic environment to address causal discovery questions and deliver plausible causal graphs. We evaluate the ALCM framework by implementing two demonstrations on seven well-known datasets. Experimental results demonstrate that ALCM outperforms existing LLM methods and conventional data-driven causal reasoning mechanisms. This study not only shows the effectiveness of the ALCM but also underscores new research directions in leveraging the causal reasoning capabilities of LLMs.

翻译：在高维数据集中进行有效因果推理时，必须首先启动因果发现过程，即基于观测数据生成因果图。然而，获取完整且准确的因果图是一项艰巨的挑战，被公认为NP-hard问题。近年来，大语言模型的出现开启了新时代，展现出其在医学、金融、科学等不同领域促进因果推理的涌现能力与广泛适用性。大语言模型庞大的知识库通过提供可解释性、推理能力、泛化性以及发现新因果结构，具有提升因果推理领域的潜力。本文提出了一种名为"自主大语言模型增强的因果发现框架"的新框架，旨在协同数据驱动的因果发现算法与大语言模型，自动生成更具鲁棒性、准确性和可解释性的因果图。该框架包含三个核心组件：因果结构学习、因果包装器以及大语言模型驱动的因果精炼器。这些组件在动态环境中自主协作，以解答因果发现问题并提供合理的因果图。我们在七个知名数据集上通过两个演示实验对ALCM框架进行了评估。实验结果表明，ALCM优于现有的大语言模型方法和传统数据驱动因果推理机制。本研究不仅展示了ALCM的有效性，还强调了利用大语言模型因果推理能力的新研究方向。