RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model
RealTCD：结合大语言模型的干预数据时序因果发现AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要的核心任务是从干预数据识别时序因果结构，并利用这些关系进行根因分析。
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RealTCD：结合大语言模型的干预数据时序因果发现
- 研究问题
- 工业运维系统中，当干预目标未知且系统文本丰富复杂时，如何利用干预数据与领域知识发现时序因果关系，服务于根因分析？
- 方法与思路
- 作者提出 RealTCD，以策略性掩码和正则化构建不依赖已知干预目标的评分式时序发现方法，再用大语言模型提取文本知识并指导元初始化。
- 材料陈述的贡献
- 摘要称，框架将未知干预目标下的结构发现与系统文本知识整合，并在模拟及真实数据实验中取得优于所比较基线的因果结构发现表现。
- 阅读关注点
- 应核对未知干预目标下的识别依据、评分函数与掩码作用、文本知识如何影响初始化，以及实验真值、评价指标和消融设计。
- 阅读边界
- 依据所给 arXiv 预印本摘要，不能确认同行评审或期刊接收。摘要未说明完整识别假设、实验数值及文本知识错误时的鲁棒性。
原文摘要
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.