Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis
This addresses the challenge of root cause analysis in operational systems like logistics and IoT, where traditional methods fail to scale, but it appears incremental as it adapts existing causal inference to larger graphs.
The paper tackles the problem of scaling causal mediation analysis for complex systems with high-dimensional directed acyclic graphs, proposing a framework that decomposes total effects into direct and indirect components, and demonstrates its utility in fulfillment center logistics case studies.
Modern operational systems ranging from logistics and cloud infrastructure to industrial IoT, are governed by complex, interdependent processes. Understanding how interventions propagate through such systems requires causal inference methods that go beyond direct effects to quantify mediated pathways. Traditional mediation analysis, while effective in simple settings, fails to scale to the high-dimensional directed acyclic graphs (DAGs) encountered in practice, particularly when multiple treatments and mediators interact. In this paper, we propose a scalable mediation analysis framework tailored for large causal DAGs involving multiple treatments and mediators. Our approach systematically decomposes total effects into interpretable direct and indirect components. We demonstrate its practical utility through applied case studies in fulfillment center logistics, where complex dependencies and non-controllable factors often obscure root causes.