CNs are directed graphs representing cause-effect (CE) relations and are multiple response diagrams. The paper explains transformation of a CN into a causal map, which represents all factors and responses as points in a common D-dimensional metric space. An algorithmic approach is used, optimizing the entropy criterion to maximize dispersion among the multiple responses, using a distance-in-space coefficients model. A key constraint is for the blocks to be self-contained implying that each block can be analyzed without reference to the others. This is to be complemented by a unified, all-block analysis. The resulting designs are evaluated for efficiency .response dispersion and resolution V column rank.
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