Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Machine Learning
2022-10-27 v1 Artificial Intelligence
Machine Learning
Abstract
Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to existing applications, we do not assume linear relationships leading to more informative results.
Cite
@article{arxiv.2210.14573,
title = {Learning Causal Graphs in Manufacturing Domains using Structural Equation Models},
author = {Maximilian Kertel and Stefan Harmeling and Markus Pauly},
journal= {arXiv preprint arXiv:2210.14573},
year = {2022}
}
Comments
To be published in the Proceedings of IEEE AI4I 2022