Group-Driven Causal Modeling and Root Cause Analysis for High-Dimensional Time Series

Root Cause Analysis (RCA) is vital for industrial monitoring and network diagnosis. In highdimensional scenarios, traditional causal modeling is susceptible to noise and redundant dependencies, leading to unstable causal structures and poor interpretability. This paper proposes a novel method, GCRA (Group-Driven Causal Modeling and Root Cause Analysis), to address these challenges. Firstly, the method utilizes cross-correlation to construct variable groups, establishing a group-level causal graph to effectively manage complex dependencies. Secondly, a mapping mechanism gradually refines this group-level graph into a variable-level structure. This structure guides the reconstruction of time-series observations, while structure-guided predictors are designed to model exogenous interventions representing unpredictable external disturbances. Finally, finegrained RCA is achieved by calculating attribution scores based on these exogenous interventions. Experimental results across three synthetic and one real-world dataset demonstrate that GCRA accurately captures causal relationships and identifies root causes. The findings validate that grouplevel construction and structure-guided modeling significantly stabilize causal structures and enhance localization accuracy in high-dimensional data.