Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models
Machine Learning
2025-09-03 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2509.02535,
title = {Probabilities of Causation and Root Cause Analysis with Quasi-Markovian Models},
author = {Eduardo Rocha Laurentino and Fabio Gagliardi Cozman and Denis Deratani Maua and Daniel Angelo Esteves Lawand and Davi Goncalves Bezerra Coelho and Lucas Martins Marques},
journal= {arXiv preprint arXiv:2509.02535},
year = {2025}
}
Comments
Accepted at the 35th Brazilian Conference on Intelligent Systems (BRACIS 2025)