LLM-Enhanced Causal Discovery in Temporal Domain from Interventional Data

arXiv:2404.14786v1 Announce Type: cross
Abstract: 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, …

abstract analysis artificial artificial intelligence arxiv causal construction cs.ai cs.lg data discovery domain graph identify industrial information information technology intelligence llm maintenance operations pivotal relationships root cause analysis stat.me tasks technology temporal type variables