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

2025/09/02 by Eduardo Rocha Laurentino, Fábio Gagliardi Cozman, Laurentino, Eduardo Rocha +7
Computer Science · Mathematics · #Advanced Causal Inference Techniques #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2509.02535

openalex publication_date 2025/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends
• Efficient Computation of Counterfactual Bounds
• Partial Counterfactual Identification from Observational and Experimental Data
• Causal structure based root cause analysis of outliers
• What Counterfactuals Can Be Tested
• Counterfactual Probabilities: Computational Methods, Bounds and Applications

Related

• Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds
• Probabilities of Causation: Bounds and Identification
• A causation coefficient and taxonomy of correlation/causation\n relationships
• Sample-Specific Root Causal Inference with Latent Variables
• Probabilities of Causation for Continuous and Vector Variables