Actual causality in fault trees

2026-07-02

AI Summary

Traditional fault trees identify only pathways that “could go wrong” but struggle to explain “why a failure actually occurred,” lacking a rigorous representation of actual causality. This work proposes the first systematic integration of Halpern and Pearl’s theory of actual causation into fault tree analysis. By unifying Boolean logic, graph theory, and formal causal reasoning, the approach establishes a precise correspondence between minimal cut sets and actual causes, and provides a comprehensive classification of distinct actual causation scenarios. The method bridges the gap between potential failures and actual attribution, offering both a solid theoretical foundation and a practical tool for diagnosing faults in complex systems.

Abstract

Fault trees are a widely used as effective risk models for complex systems, answering the question "what can go wrong?", especially through minimal cut set analysis. We study fault trees from the perspective of Halpern & Pearl's theory of actual causality. This allows us to use fault trees to answer the question "why has it gone wrong?", which is fundamental to failure diagnostics. We give a complete classification of each of the different notions of actual causality in terms of the fault tree's graph structure and logical structure, and show how minimal cut sets give rise to actual causes.

Problem

• actual causality
• fault trees
• failure diagnostics
• minimal cut sets
• risk models

Innovation

• actual causality
• fault trees
• minimal cut sets
• failure diagnostics
• causal reasoning