论文标题
使用结构方程模型在制造域中学习因果图
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
论文作者
论文摘要
许多生产过程的特征是许多复杂的因果关系。由于它们仅部分知道，因此它们对有效的过程控制构成了挑战。在这项工作中，我们介绍了如何将结构方程模型用于从制造领域中的先验知识和过程数据的组合得出因果关系。与现有应用程序相比，我们不假设线性关系会带来更有信息的结果。
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.
