# Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/learning-causal-graphs-in-manufacturing-domains-using-structural-equation-models/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Maximilian Kertel,Stefan Harmeling,Markus Pauly |
| Citations | 0 |
| DOI | 10.48550/arxiv.2210.14573 |
| Fields | Computer Science,Engineering |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2210.14573 |
| OpenAlex ID | https://openalex.org/W4307474825 |
| Type | preprint |
| Year | 2022 |

## Paper authors

- [Maximilian Kertel](https://scholariq.org/researchers/maximilian-kertel/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## Paper primary topic

- [Bayesian Modeling and Causal Inference](https://scholariq.org/topics/bayesian-modeling-and-causal-inference/)

## Paper topics

- [Bayesian Modeling and Causal Inference](https://scholariq.org/topics/bayesian-modeling-and-causal-inference/)
- [AI-based Problem Solving and Planning](https://scholariq.org/topics/ai-based-problem-solving-and-planning/)
- [Fault Detection and Control Systems](https://scholariq.org/topics/fault-detection-and-control-systems/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.