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Probabilities of causation provide principled ways to assess causal relationships but face computational challenges due to partial identifiability and latent confounding. This paper introduces both algorithmic simplifications, significantly reducing the computational complexity of calculating tighter bounds for these probabilities, and a novel methodological framework for Root Cause Analysis that systematically employs these causal metrics to rank entire causal paths.

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Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models

  • 会议论文

  • 发表日期：

    2026年

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  • Eduardo Rocha Laurentino,
  • Fabio Gagliardi Cozman,
  • Denis Deratani Mauá,
  • Daniel Angelo Esteves Lawand,
  • Davi Goncalves Bezerra Coelho,
  • Lucas Martins Marques

摘要

Probabilities of causation provide principled ways to assess causal relationships but face computational challenges due to partial identifiability and latent confounding. This paper introduces both algorithmic simplifications, significantly reducing the computational complexity of calculating tighter bounds for these probabilities, and a novel methodological framework for Root Cause Analysis that systematically employs these causal metrics to rank entire causal paths.