# Actual causality in fault trees

arXiv: https://arxiv.org/abs/2607.01840 · HTML: https://mlsanity.com/paper/2607.01840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [A Causal Analysis of Harm](https://mlsanity.com/paper/2210.05327/) (2022)
- [Causality in Configurable Software Systems](https://mlsanity.com/paper/2201.07280/) (2022)
- [The Computational Complexity of Structure-Based Causality](https://mlsanity.com/paper/1412.3076/) (2014)
- [Causes and Explanations: A Structural-Model Approach --- Part 1: Causes](https://mlsanity.com/paper/1301.2275/) (2013)
- [Efficient and Generic Algorithms for Quantitative Attack Tree Analysis](https://mlsanity.com/paper/2212.05358/) (2022)
- [Intervention and Conditioning in Causal Bayesian Networks](https://mlsanity.com/paper/2405.14728/) (2024)
- [BDDs Strike Back: Efficient Analysis of Static and Dynamic Fault Trees](https://mlsanity.com/paper/2202.02829/) (2022)

## Abstract (arXiv)

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.