arXiv:2606.05636v1 Announce Type: new Abstract: Root-Cause Analysis (RCA) seeks to identify the variables responsible for abnormal system behavior in complex domains such as manufacturing, cloud computing, and healthcare. Existing approaches face a critical bottleneck: graph-based causal methods can identify intervention targets but typically require a known or accurately estimated causal graph, while graph-free statistical methods either localize marginal anomalies rather than structural causes, or rely on restrictive assumptions about graph structure or functional form. We propose StableRCA,
The increasing complexity of AI systems, cloud infrastructure, and advanced manufacturing necessitates more robust and generalizable root cause analysis methods, driving demand for innovations beyond current graph-based or statistical limitations.
Improved root cause analysis tools like StableRCA are critical for maintaining reliability and optimizing performance in complex AI-driven systems across various industries, directly impacting operational efficiency and economic output.
The ability to perform robust, graph-agnostic, mechanism-level root cause analysis removes significant barriers for identifying and resolving 'why' abnormalities occur, making complex systems more manageable and resilient.
- · Cloud computing providers
- · Manufacturing sector
- · Healthcare systems
- · AI/ML operations teams
- · Legacy statistical RCA vendors
- · Organizations with opaque IT systems
System outages and performance degradations are identified and resolved faster, reducing downtime and operational costs.
The increased reliability of AI systems fosters greater adoption in critical infrastructure and expands the scope of automation.
More resilient AI and industrial systems contribute to overall economic stability and potentially accelerate technological advancement across multiple sectors by reducing risk.
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Read at arXiv cs.LG