# Anwendung von Causal-Discovery-Algorithmen zur Root-Cause-Analyse in der Fahrzeugmontage

arXiv: https://arxiv.org/abs/2407.16388 · HTML: https://mlsanity.com/paper/2407.16388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 23 Jul 2024.

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

- [DAGs with NO TEARS: Continuous Optimization for Structure Learning](https://mlsanity.com/paper/1803.01422/) (2018)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization](https://mlsanity.com/paper/2209.08037/) (2022)
- [gCastle: A Python Toolbox for Causal Discovery](https://mlsanity.com/paper/2111.15155/) (2021)
- [textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery](https://mlsanity.com/paper/2306.10816/) (2023)
- [Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs](https://mlsanity.com/paper/2402.00043/) (2024)
- [Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis](https://mlsanity.com/paper/2406.18114/) (2024)

## Built on by (most cited first, 1 of 1)

- [Sensitivity analysis of image classification models using generalized polynomial chaos](https://mlsanity.com/paper/2506.18751/) (2025)

## Abstract (arXiv)

Root Cause Analysis (RCA) is a quality management method that aims to systematically investigate and identify the cause-and-effect relationships of problems and their underlying causes. Traditional methods are based on the analysis of problems by subject matter experts. In modern production processes, large amounts of data are collected. For this reason, increasingly computer-aided and data-driven methods are used for RCA. One of these methods are Causal Discovery Algorithms (CDA). This publication demonstrates the application of CDA on data from the assembly of a leading automotive manufacturer. The algorithms used learn the causal structure between the characteristics of the manufactured vehicles, the ergonomics and the temporal scope of the involved assembly processes, and quality-relevant product features based on representative data. This publication compares various CDAs in terms of their suitability in the context of quality management. For this purpose, the causal structures learned by the algorithms as well as their runtime are compared. This publication provides a contribution to quality management and demonstrates how CDAs can be used for RCA in assembly processes.