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

TL;DR

This paper introduces a scalable causal mediation framework designed for large, complex systems with multiple treatments and mediators, enabling better root cause analysis in operational environments.

Contribution

It presents a novel, scalable method for mediation analysis applicable to high-dimensional causal DAGs with multiple interacting treatments and mediators.

Findings

Successfully applied to logistics case studies

Effectively decomposes total effects into direct and indirect components

Enhances understanding of complex causal dependencies

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.