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Optimizing a control plan using a structural equation model with an application to statistical process analysis

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  • Manabu Kuroki

Abstract

In the case where non-experimental data are available from an industrial process and a directed graph for how various factors affect a response variable is known based on a substantive understanding of the process, we consider a problem in which a control plan involving multiple treatment variables is conducted in order to bring a response variable close to a target value with variation reduction. Using statistical causal analysis with linear (recursive and non-recursive) structural equation models, we configure an optimal control plan involving multiple treatment variables through causal parameters. Based on the formulation, we clarify the causal mechanism for how the variance of a response variable changes when the control plan is conducted. The results enable us to evaluate the effect of a control plan on the variance of a response variable from non-experimental data and provide a new application of linear structural equation models to engineering science.

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  • Manabu Kuroki, 2012. "Optimizing a control plan using a structural equation model with an application to statistical process analysis," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 673-694, August.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:3:p:673-694
    DOI: 10.1080/02664763.2011.610444
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    Cited by:

    1. Nanmo, Hisayoshi & Kuroki, Manabu, 2021. "Exact variance formula for the estimated mean outcome with external intervention based on the front-door criterion in Gaussian linear structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 185(C).

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