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D-Optimal Design for a Causal Structure for Completely Randomized and Random Blocked Experiments

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  • Zaher Kmail
  • Kent Eskridge
  • Muhammad Ahsan

Abstract

Most experimental design literature on causal inference focuses on establishing a causal relationship between variables, but there is no literature on how to identify a design that results in the optimal parameter estimates for a structural equation model (SEM). In this research, search algorithms are used to produce a D-optimal design for a SEM for three-stage least squares and full information maximum likelihood estimators. Then, a D-optimal design for the estimate of the model parameters of a mixed-effects SEM is obtained. The efficiency of each of the D-optimal designs for SEMs is compared with univariate optimal and uniform designs. In each case, the causal relationship changed the optimal designs dramatically and the new D-optimal designs were more efficient.

Suggested Citation

  • Zaher Kmail & Kent Eskridge & Muhammad Ahsan, 2022. "D-Optimal Design for a Causal Structure for Completely Randomized and Random Blocked Experiments," Journal of Probability and Statistics, Hindawi, vol. 2022, pages 1-15, August.
    • Handle: RePEc:hin:jnljps:7299086
    DOI: 10.1155/2022/7299086
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