Causal modeling alternatives in operations research: Overview and application

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.

[1]  K. Mardia Measures of multivariate skewness and kurtosis with applications , 1970 .

[2]  C. Spearman General intelligence Objectively Determined and Measured , 1904 .

[3]  Michael I. Jordan Learning in Graphical Models , 1999, NATO ASI Series.

[4]  J. W. Dunlap,et al.  The Vectors of the Mind , 1937 .

[5]  Gyula Vastag,et al.  Linkages among manufacturing concepts, inventories, delivery service and competitiveness , 2001 .

[6]  T. Little,et al.  To Parcel or Not to Parcel: Exploring the Question, Weighing the Merits , 2002 .

[7]  T. Bayes An essay towards solving a problem in the doctrine of chances , 2003 .

[8]  T. Speed,et al.  Structural Analysis of Multivariate Data: A Review , 1982 .

[9]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[10]  W. R. Dillon,et al.  Inferring Latent Brand Dependencies , 2000 .

[11]  Judea Pearl,et al.  Equivalence and Synthesis of Causal Models , 1990, UAI.

[12]  Albert Einstein,et al.  Can Quantum-Mechanical Description of Physical Reality Be Considered Complete? , 1935 .

[13]  Richard Scheines,et al.  Tetrad II: User's Manual , 1994 .

[14]  Anthony G. Greenwald,et al.  Applications of Covariance Structure Modeling in Psychology : Cause for Concern ? , 2001 .

[15]  Gregory F. Cooper,et al.  An overview of the representation and discovery of causal relationships using Bayesian networks , 1999 .

[16]  T. Cook,et al.  Quasi-experimentation: Design & analysis issues for field settings , 1979 .

[17]  Ad Feelders Introduction to Intelligent Data Analysis , 2003 .

[18]  Robert C. MacCallum,et al.  Model specification: Procedures, strategies, and related issues. , 1995 .

[19]  William S. Cleveland,et al.  Visualizing Data , 1993 .

[20]  T. Micceri The unicorn, the normal curve, and other improbable creatures. , 1989 .

[21]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.

[22]  Paola Sebastiani,et al.  Bayesian Inference with Missing Data Using Bound and Collapse , 2000 .

[23]  P. Lazarsfeld Vectors of Mind , 1937 .

[24]  David J. Spiegelhalter,et al.  Bayesian analysis in expert systems , 1993 .

[25]  P. Suppes A Probabilistic Theory Of Causality , 1970 .

[26]  D. Geiger,et al.  A characterization of the Dirichlet distribution through global and local parameter independence , 1997 .

[27]  K. G. Jöreskog,et al.  Statistical analysis of sets of congeneric tests , 1971 .

[28]  A. E. Maxwell,et al.  Factor Analysis as a Statistical Method. , 1964 .

[29]  Paola Sebastiani,et al.  Bayesian methods for intelligent data analysis , 1998 .

[30]  Herbert A. Simon,et al.  Causality in Bayesian Belief Networks , 1993, UAI.

[31]  Stephen G. West,et al.  Structural equation models with non-normal variables: Problems and remedies , 1995 .

[32]  R. L. Winkler The Assessment of Prior Distributions in Bayesian Analysis , 1967 .

[33]  L. Hayduk,et al.  Latent Variable Interaction and Quadratic Effect Estimation: A Two-Step Technique Using Structural Equation Analysis , 1996 .

[34]  Richard Scheines,et al.  Public administration and health care: estimating latent causal influences: TETRAD III variable selection and Bayesian parameter estimation , 2002 .

[35]  I. Stelzl Changing a Causal Hypothesis without Changing the Fit: some Rules for Generating Equivalent Path Models. , 1986, Multivariate behavioral research.

[36]  Mtw,et al.  Computation, causation, and discovery , 2000 .

[37]  A. Goldberger,et al.  Structural Equation Models in the Social Sciences. , 1974 .

[38]  J. Pearl Causal diagrams for empirical research , 1995 .

[39]  Pedro Larrañaga,et al.  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[40]  D. Clay Whybark,et al.  Global relations between inventory, manufacturing lead time and delivery date promises , 1993 .

[41]  Peter M. Bentler,et al.  EQS : structural equations program manual , 1989 .

[42]  Ross D. Shachter Evaluating Influence Diagrams , 1986, Oper. Res..

[43]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[44]  D. Wegener,et al.  The problem of equivalent models in applications of covariance structure analysis. , 1993, Psychological bulletin.

[45]  Willem E. Saris,et al.  Alternative approaches to structural modeling of ordinal data: A Monte Carlo study , 1997 .

[46]  R. P. McDonald,et al.  Structural Equations with Latent Variables , 1989 .

[47]  David Hume A Treatise of Human Nature: Being an Attempt to introduce the experimental Method of Reasoning into Moral Subjects , 1972 .

[48]  D. R. Johnson,et al.  Ordinal measures in multiple indicator models: A simulation study of categorization error. , 1983 .

[49]  Peter M. Bentler,et al.  Practical Issues in Structural Modeling , 1987 .

[50]  K. Jöreskog A General Method for Estimating a Linear Structural Equation System. , 1970 .

[51]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[52]  T. Bayes LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S , 1763, Philosophical Transactions of the Royal Society of London.

[53]  K. Jöreskog Statistical analysis of sets of congeneric tests , 1971 .

[54]  W. Velicer,et al.  Relation of sample size to the stability of component patterns. , 1988, Psychological bulletin.

[55]  R. Hoyle Structural equation modeling: concepts, issues, and applications , 1997 .

[56]  Emin Babakus,et al.  The Sensitivity of Confirmatory Maximum Likelihood Factor Analysis to Violations of Measurement Scale and Distributional Assumptions , 1987 .

[57]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[58]  S. Wright The Method of Path Coefficients , 1934 .

[59]  J. Woodward,et al.  Independence, Invariance and the Causal Markov Condition , 1999, The British Journal for the Philosophy of Science.

[60]  H. Reichenbach,et al.  The Direction of Time , 1959 .

[61]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.