  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.
