Actual causality in fault trees

Georgiana Caltais, Milan Lopuhaä-Zwakenberg, Mariëlle Stoelinga
7/2/2026
cs.AI

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{caltais2026actual,
  title  = {Actual causality in fault trees},
  author = {Georgiana Caltais and Milan Lopuhaä-Zwakenberg and Mariëlle Stoelinga},
  year   = {2026},
  eprint = {2607.01840},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.01840v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2607.01840.svg)](https://codeofpaper.com/paper/2607.01840)

Discussion