ALCM: Autonomous LLM-Augmented Causal Discovery Framework
ALCM：自主大语言模型增强的因果发现框架AI 解读 ↓
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ALCM：自主大语言模型增强的因果发现框架
- 研究问题
- 如何结合观测数据驱动的因果发现算法与大语言模型知识，提高因果图生成的准确性、解释性及自动化程度？
- 方法与思路
- 作者提出由因果结构学习、因果封装器和大语言模型因果修正器组成的框架，通过模块协作生成并修正候选因果图。
- 材料陈述的贡献
- 摘要称在七个常用数据集的两类演示中，该框架优于现有大语言模型方法和传统数据驱动方法，但未报告具体指标数值。
- 阅读关注点
- 应核对模型知识来源、修正图结构的规则、数据集真值与评价指标，并检查信息泄漏、提示敏感性和因果识别条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；摘要未说明识别假设或完整实验设置，生成合理图不等于验证因果关系。
原文摘要
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.