In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of graph construction, facilitating downstream industrial tasks such as root cause analysis. Temporal causal discovery, as an emerging method, aims to identify temporal causal relationships between variables directly from observations by utilizing interventional data. However, existing methods mainly focus on synthetic datasets with heavy reliance on intervention targets and ignore the textual information hidden in real-world systems, failing to conduct causal discovery for real industrial scenarios. To tackle this problem, in this paper we propose to investigate temporal causal discovery in industrial scenarios, which faces two critical challenges: 1) how to discover causal relationships without the interventional targets that are costly to obtain in practice, and 2) how to discover causal relations via leveraging the textual information in systems which can be complex yet abundant in industrial contexts. To address these challenges, we propose the RealTCD framework, which is able to leverage domain knowledge to discover temporal causal relationships without interventional targets. Specifically, we first develop a score-based temporal causal discovery method capable of discovering causal relations for root cause analysis without relying on interventional targets through strategic masking and regularization. Furthermore, by employing Large Language Models (LLMs) to handle texts and integrate domain knowledge, we introduce LLM-guided meta-initialization to extract the meta-knowledge from textual information hidden in systems to boost the quality of discovery. We conduct extensive experiments on simulation and real-world datasets to show the superiority of our proposed RealTCD framework over existing baselines in discovering temporal causal structures.

翻译：在智能运维领域，因果发现对于运维图谱构建至关重要，能够有效支撑根因分析等下游工业任务。时间因果发现作为一种新兴方法，旨在利用干预数据直接从观测中识别变量间的时间因果关系。然而，现有方法主要集中于合成数据集，且高度依赖干预目标，忽略了实际系统中隐藏的文本信息，难以应用于真实工业场景的因果发现。为应对这一问题，本文致力于研究工业场景下的时间因果发现，其面临两大关键挑战：1）如何在缺乏干预目标（实践中获取成本高昂）的情况下发现因果关系；2）如何利用系统中可能复杂但丰富的文本信息来发现因果关系。针对这些挑战，我们提出了RealTCD框架，该框架能够借助领域知识在无干预目标的情况下发现时间因果关系。具体而言，我们首先提出一种基于评分的时间因果发现方法，该方法通过策略性掩码与正则化技术，可在不依赖干预目标的情况下实现面向根因分析的因果关系发现。进一步，通过运用大语言模型处理文本并融合领域知识，我们引入了LLM引导的元初始化机制，从系统隐含的文本信息中提取元知识以提升发现质量。我们在仿真与真实数据集上进行了大量实验，结果表明所提出的RealTCD框架在时间因果结构发现方面优于现有基线方法。