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：摘要：在人工智能驱动的信息技术运维领域，因果发现对运维知识图谱构建及根因分析等下游工业任务至关重要。时域因果发现作为一种新兴方法，旨在利用干预数据直接观测变量间的时序因果关系。然而现有方法主要关注合成数据集，过度依赖干预目标，且忽略现实系统中隐含的文本信息，难以适用于真实工业场景的因果发现。针对该问题，本文提出面向工业场景的时域因果发现研究，面临两大关键挑战：1）如何在实践中难以获取干预目标的情况下发现因果关系；2）如何利用工业系统中复杂但丰富的文本信息进行因果发现。为应对这些挑战，我们提出RealTCD框架，该框架能够利用领域知识，在无需干预目标的情况下发现时序因果关系。具体而言，我们首先开发了一种基于评分的时域因果发现方法，通过策略性掩码和正则化技术，无需依赖干预目标即可发现根因分析所需的因果关联。进一步，通过采用大语言模型处理文本并整合领域知识，我们引入大语言模型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。在仿真数据集和真实数据集上的大量实验表明，所提出的RealTCD框架在时序因果结构发现方面显著优于现有基线方法。