发表机构
University of Technology Sydney（悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高专业领域非结构化数据因果发现难题，提出DKCD框架，通过知识挖掘、知识引导的因果推理和因果结构发现三个组件，有效应对潜在因素识别不足和因素注释不可靠问题，实验证明其显著提升了因果发现效果。
AI 中文摘要
从非结构化数据中进行因果发现是医疗、金融和教育等高专业领域中一项具有挑战性但未充分探索的任务。现有方法利用大语言模型的一般知识从非结构化数据中识别因果因素并注释到结构化数据中以构建因果图，但受两个关键挑战限制：一是因缺乏领域特定知识而对潜在因素识别不足，二是因缺乏领域基础推理导致因素注释不可靠。为此引入了DKCD框架，包含知识挖掘、知识引导的因果推理和因果结构发现三个相互关联的组件。在两个特定领域数据集上的实验表明，DKCD显著改善了因果因素识别和因果图构建。
英文摘要
Causal discovery from unstructured data is a challenging yet underexplored task in high-expertise domains such as healthcare, finance, and education. Existing methods typically leverage the general knowledge of large language models (LLMs) to identify causal factors from unstructured data and annotate them into structured data for causal graph construction. However, they remain limited by two key challenges (CHs): (CH1) insufficient identification of latent factors, which are implicit in the data yet essential for causal discovery, due to the lack of domain-specific knowledge; and (CH2) unreliable factor annotation, caused by the lack of domain-grounded reasoning, which propagates errors to the resulting causal graphs. To address these challenges, we introduce a novel Domain Knowledge-enhanced Causal Discovery framework (DKCD) for causal discovery from unstructured data in high-expertise domains with three interconnected components: (1) Knowledge Mining: It retrieves relevant domain knowledge based on observable factors to support subsequent causal reasoning. (2) Knowledge-guided Causal Reasoning: Reasoning with relevant knowledge, it discovers latent causal factors to address CH1 and generates key causal clues for more accurate data annotation to address CH2. (3) Causal Structure Discovery: It constructs the final causal graphs based on a more complete factor set and accurate annotations. Experiments on two domain-specific datasets show that DKCD significantly improves both causal factor identification and causal graph construction.
