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Designing Experiments for Causal Networks
William D. Heavlin
Vol. 45, No. 2 (May, 2003), pp. 115-129
Published by: Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality
Stable URL: http://www.jstor.org/stable/25047009
Page Count: 15
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Causal networks are directed graphs representing cause-effect relationships and are multiple-response generalizations of Ishikawa's cause-effect diagrams. Emphasizing tolerance design applications, this article describes an algorithm for designing suitable experiments when the factors and responses are organized as a causal network. The causal network is transformed into a so-called causal map, which represents all factors and responses as points in a common D-dimensional metric space. The design approach is algorithmic, optimizing the entropy criterion due to Wynn. This criterion is applied 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; this implies that each block can be analyzed without reference to other blocks. 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. Particular attention is given to skewing each block by shifting one or a few factors off-center.
Technometrics © 2003 American Statistical Association