Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
This work addresses process control issues in manufacturing, but it appears incremental as it extends existing methods by relaxing linearity assumptions.
The paper tackled the challenge of unknown cause-and-effect relationships in manufacturing processes by using Structural Equation Models to derive these relationships from prior knowledge and process data, resulting in more informative outcomes by not assuming linear relationships.
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.