DESIGNING EXPERIMENTS FOR CAUSAL NETWORKS

机译：用于因果网络的实验

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Causal networks are directed graphs that generalize Ishikawa diagrams to encompass multiple responses. Emphasizing tolerance design applications, this work presents an optimal design algorithm when the variables are organized as a causal network. The causal network is first transformed into a causal map, which represents all factors and responses as points in a common D-dimensional metric space. The design approach is algorithmic, optimizing Wynn’s entropy criterion. This criterion maximizes dispersion among predicted multivatiate responses, using a distance-inspace coefficients (DiSCo) model. A key constraint is block self-containment—the blocks are analyzable without reference to one another; these analyses are to be complemented by a unified all-block analysis. Also explored is the benefit of skewing blocks by setting a few factors off-target.

机译：因果网络是指示图，它概括了Ishikawa图来包含多个响应。强调公差设计应用，当变量组织为因果网络时，这项工作提出了最佳设计算法。原因网络首先转换为因果映射，表示所有因素和响应作为共同的D维度量空间中的点。设计方法是算法，优化Wynn的熵标准。该标准使用距离Inspace系数（Disco）模型来最大化预测的多方响应之间的分散。关键约束是块自我密封 - 块可在不参考彼此的情况下进行分析;这些分析应通过统一的全块分析补充。还探讨是偏离目标的几个因素来偏斜块的好处。

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《Symposium on the interface》|2002年||共4页
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中图分类计算技术、计算机技术;
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blocking; directed graphs; multidimensional scaling; optimal design; tolerance design.;

机译：阻止;有向图;多维缩放;最优设计;公差设计。;

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专利

1. Designing experiments for causal networks [J] . William D. Heavlin Quality Control and Applied Statistics . 2005,第4期

机译：设计因果网络的实验
2. Designing Experiments for Causal Networks [J] . William D. HEAVLIN Technometrics . 2003,第2期

机译：因果网络的设计实验
3. Active Learning of Causal Networks with Intervention Experiments and Optimal Designs [J] . He Yang-Bo, Geng Zhi Journal of machine learning research . 2008,第Nov期

机译：通过干预实验和优化设计主动学习因果网络
4. DESIGNING EXPERIMENTS FOR CAUSAL NETWORKS [C] . Symposium on the interface . 2002

机译：用于因果网络的实验
5. Causal not confounded gene networks: Inferring acyclic and non-acyclic gene Bayesian networks in mRNA expression studies using recursive V-structures, genetic variation, and orthogonal causal anchor structural equation models. [D] . Aten, Jason Erik. 2008

机译：因果不混淆的基因网络：使用递归V型结构，遗传变异和正交因果锚结构方程模型，在mRNA表达研究中推断无环和非无环基因贝叶斯网络。
6. Designing experiments to understand the variability in biochemical reaction networks [O] . Jakob Ruess, Andreas Milias-Argeitis, John Lygeros 2013

机译：设计实验以了解生化反应网络中的可变性
7. Learning causal bayesian networks from observations and experiments: A decision theoretic approach [O] . Stijn Meganck, Bernard Manderick et al. 2006

机译：从观察和实验中学习因果贝叶斯网络：决策理论方法

DESIGNING EXPERIMENTS FOR CAUSAL NETWORKS

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