RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model

作者：Peiwen Li, Xin Wang, Zeyang Zhang, Yuan Meng, Fang Shen, Y Li, Jialong Wang, Yang Li, Wenwu Zhu · 年份：2024 · DOI：10.1145/3627673.3680042 · 被引用次数：12 · 研究领域：Topic Modeling、Data Quality and Management、Bayesian Modeling and Causal Inference

In the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of systems, facilitating downstream industrial tasks such as root cause analysis. Temporal causal discovery, as an emerging method, aims to identify temporal causal relations between variables directly from observations by utilizing interventional data. However, existing methods mainly focus on synthetic datasets with heavy reliance on interventional 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 investigate temporal causal discovery in industrial scenarios, which faces two critical challenges: how to discover causal relations without the interventional targets that are costly to obtain in practice, and 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 relations without interventional targets. We first develop a score-based temporal causal discovery method capable of discovering causal relations without relying on interventional targets through strategic masking and regularization. Then, by employing Large Language Models (LLMs) to handle texts and integrate domain knowledge, we introduce ...