Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis

2025-12-15

AI Summary

Root cause localization in complex systems—such as logistics, cloud infrastructure, and industrial IoT—is hindered by intertwined multiple treatment and mediator variables under high-dimensional causal DAGs. Method: We propose the first scalable causal mediation analysis framework for large-scale DAGs, grounded in structural causal models (SCMs). It enables modular effect decomposition, path-level causal contribution quantification, approximate optimal mediator identification, and robust adjustment for uncontrolled confounders. Contribution/Results: Our approach overcomes classical mediation analysis limitations in dimensionality and graph complexity, supporting joint modeling of multiple treatments and mediators. Evaluated on real-world fulfillment center data, it improves indirect effect identification accuracy by 37% and achieves ∼100× speedup over baseline methods, significantly enhancing both precision and scalability of root cause diagnosis in high-dimensional systems.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• Scaling causal mediation for complex, high-dimensional systems.
• Decomposing total effects into direct and indirect components.
• Identifying root causes in interdependent processes like logistics.

Innovation

• Scalable mediation analysis for large causal DAGs
• Decomposes total effects into direct and indirect components
• Tailored for multiple treatments and mediators in complex systems