Causal modeling alternatives in operations research: Overview and application

Causal modeling alternatives in operations research: Overview and application

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Article ID: iaor20052111
Country: Netherlands
Volume: 156
Issue: 1
Start Page Number: 92
End Page Number: 109
Publication Date: Jul 2004
Journal: European Journal of Operational Research
Authors: ,
Keywords: networks, statistics: general
Abstract:

This paper uses the relationships between three basic, fundamental and proven concepts in manufacturing (resource commitment to improvement programs, flexibility to changes in operations and customer delivery performance) as the empirical context for reviewing and comparing two casual modeling approaches (structural equation modeling and Bayesian networks). Specifically, investments in total quality management (TQM), process analysis, and employee participation programs are considered as resource commitments. The paper begins with the central issue of the requirements for a model of associations to be considered causal. This philosophical issue is addressed in reference to probabilistic causation theory. Then, each method is reviewed in the context of a unified causal modeling framework consistent with probabilistic causation theory and applied to a common dataset. The comparisons include concept representation, distribution and functional assumptions, sample size and model complexity considerations, measurement issues, specification search, model adequacy, theory testing and inference capabilities. The paper concludes with a summary of relative advantages and disadvantages of the methods and highlights the findings relevant to the literature on TQM and on-time deliveries.

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