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

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

Resumo

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.