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

Author

Listed:
  • Alessandro Casadei
  • Sreyoshi Bhaduri
  • Rohit Malshe
  • Pavan Mullapudi
  • Raj Ratan
  • Ankush Pole
  • Arkajit Rakshit

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.

Suggested Citation

  • Alessandro Casadei & Sreyoshi Bhaduri & Rohit Malshe & Pavan Mullapudi & Raj Ratan & Ankush Pole & Arkajit Rakshit, 2025. "Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis," Papers 2512.14764, arXiv.org.
    • Handle: RePEc:arx:papers:2512.14764
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    References listed on IDEAS

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    1. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    2. Tikka, Santtu & Karvanen, Juha, 2017. "Identifying Causal Effects with the R Package causaleffect," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i12).
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