arXiv Open Access 2022

Learning Causal Graphs in Manufacturing Domains using Structural Equation Models

Maximilian Kertel Stefan Harmeling Markus Pauly

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

stat.ML cs.AI cs.LG

Penulis (3)

Maximilian Kertel

Stefan Harmeling

Markus Pauly

Format Sitasi

APA MLA BibTeX

Kertel, M., Harmeling, S., Pauly, M. (2022). Learning Causal Graphs in Manufacturing Domains using Structural Equation Models. https://arxiv.org/abs/2210.14573

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓