Causal modeling alternatives in operations research: Overview and application

European Journal of Operational Research

Volumn 156, Issue 1, 2004, Pages 92-109

  Anderson, Ronald D ^a   Vastag, Gyula ^a  

^a INDIANA UNIVERSITY   (United States)

Author keywords

Bayesian networks; Causal modeling; Delivery performance; Manufacturing; Structural equation modeling; TQM

Indexed keywords

COMPUTER SCIENCE; GRAPH THEORY; MANAGERS; MARKETING; MATHEMATICAL MODELS; PERSONNEL; PROBABILITY; TOTAL QUALITY MANAGEMENT;

BAYESIAN NETWORKS; CASUAL MODELING; DELIVERY PERFORMANCE; STRUCTURAL EQUATION MODELING;

OPERATIONS RESEARCH;

EID: 1242321013     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(02)00904-9     Document Type: Conference Paper

References (64)

1. Acito F., Anderson R.D. Factor score indeterminacy: Correlates and implications for marketing research. Journal of Marketing Research. 20(2):1986;111-118.
2. Babakus E., Ferguson C.E., Jöreskog K.G. The sensitivity of confirmatory maximum likelihood factor analysis to violations of measurement scale and distribution assumptions. Journal of Marketing Research. 37:1987;72-141.
3. Bayes T. An essay toward solving a problem in the doctrine of chance. Philosophical Transactions of the Royal Society. 53:1763;370-418.
4. Bentler P.M. EQS Structural Equations Program Manual. 1995;Multivariate Software, Encino, CA.
5. Bentler P.M., Chou C.P. Practical issues in structural modeling. Sociological Methods and Research. 16:1987;78-117.
6. Bernardo J.M., Smith A.F.M. Bayesian Theory. 2000;Wiley, New York.
7. Bollen K.A. Structural Equations with Latent Variables. 1989;Wiley, New York.
8. Breckler S.J. Application of covariance structure modeling in psychology: Cause for concern? Psychological Bulletin. 107:1990;260-273.
9. Cartwright N. What is a causal structure? McKim V.R., Turner P.T. Causality in Crisis? 1997;343-357 University of Notre Dame Press, Notre Dame, IN.
10. Cleveland W.S. Visualizing Data. 1993;Hobart Press, Summit, NJ.
11. Coenders G., Satorra A., Saris W.E. Alternative approaches to structural modeling of ordinal data. Structural Equation Modeling. 4(4):1997;261-282.
12. Cook T.D., Campbell D.T. Quasi-Experimentation: Design and Analysis Issues for Field Settings. 1979;Rand McNally, Chicago.
13. Cooper G.F. An overview of the representation and discovery of causal relationships using bayesian networks. Glymour C., Cooper G.F. Computation, Causation and Discovery. 1999;3-62 MIT Press, Menlo Park, CA.
14. Cooper G.F., Herskovits E. A Bayesian method for the induction of probabilistic network from data. Machine Learning. 9:1992;309-347.
15. Cowell R.G., Dawid A.P., Lauritzen S.L., Spiegelhalter D.J. Probabilistic Networks and Expert Systems. 1999;Springer-Verlag, New York.
16. Druzdzel M.J., Simon H.A. Causality in Bayesian belief networks. Proceedings of Ninth Annual Conference in Artificial Intelligence. 1993;3-11 Morgan Kaufmann Publishers, San Francisco.
17. Einstein A., Podolsky B., Rosen N. Can quantum-mechanical description of physical reality be considered complete? Physical Review. 41:1935;777.
18. Geiger D., Herkerman D. A characterization of Dirichlet distributions through local and global independence. Annals of Statistics. 25:1997;1344-1368.
19. Glymour C., Cooper G.F. Computation, Causation and Discovery. 1999;MIT Press, Cambridge, MA.
20. Guadognoli E., Velicer W.F. Relationship of sample size to the stability of component patterns. Psychological Bulletin. 103:1988;265-275.
21. Hausman D.M., Woodward J. Independence, invariance and the causal Markov condition. British Journal for the Philosophy of Science. 50:1999;521-583.
22. Heckerman D. A tutorial on learning with Bayesian networks. Jordan M.I. Learning in Graphical Models. 1999;301-354 MIT Press, Cambridge, MA.
23. Hume D. A Treatise of Human Nature. 1969;Penguin Books, London.
24. Jensen F. An Introduction to Bayesian Network. 1996;University College London Press, London.
25. Johnson D.R., Creech J.C. Ordinal measures in multiple indicator models. American Sociological Review. 48:1983;398-407.
26. Jöreskog K.G. Statistical analysis of set of congeneric test. Psychometrika. 36:1971;109-133.
27. Jöreskog K.G. A general method for estimating a linear structural equation system. Goldberger A.S., Duncan O.D. Structural Equation Models in the Social Sciences. 1973;85-112 Academic Press, New York.
28. Jöreskog, K.G., Sörbom, D., 1993. LISREL 8, Analysis of Linear Structural Relations by the Method of Maximum Likelihood. International Education Services, Chicago.
29. Kiiveri H.T., Speed T.P. Structural analysis of multivariate data: A review. Leinhardt S. Sociological Methodology. 1982;209-289 Jossy Bass, San Francisco.
30. Larranaga P., Poza M., Yurramendi Y., Murga R.H., Kuijpers C.M.H. Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters. IEEE Journal on Pattern Analysis and Machine Intelligence. 18(9):1996;912-926.
31. Lawley D.N., Maxwell A.E. Factor Analysis as a Statistical Method. 1963;Butterworth, London.
32. Little T.D., Cunningham W.A., Shahar G., Widaman K.F. To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling. 9:2002;151-173.
33. MacCallum R.C. Model specification: Procedures, strategies, and related issues. Hoyle R.H. Structural Equation Modeling: Concepts, Issues and Applications. 1995;16-36 Sage, Thousand Oaks, CA.
34. MacCallum R.C., Wegener D.T., Uchino B.M., Fabrigar L.R. The problem of equivalent models in applications of covariance structure analysis. Psychological Bulletin. 114:1993;185-199.
35. Mardia K.V. Measures of multivariate skewness and kurtosis with applications. Biometrika. 57:1970;519-530.
36. McKim V.R., Turner S.P. Causality in Crisis? 1997;University of Notre Dame Press, Notre Dame, IN.
37. Micceri T. The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin. 105:1989;156-166.
38. Muthen B.O., Muthen L.K. Mplus: User's Guide. 1998;Muthen and Muthen, Los Angeles, CA.
39. Pearl J. Probabilistic Inference in Intelligent Systems. 1988;Morgan Kaufmann, San Mateo, CA.
40. Pearl J. Causal diagrams for empirical research. Biometrika. 82(4):1995;669-710.
41. Pearl J. Causality: Models, Reasoning and Inference. 2000;Cambridge University Press, Cambridge, UK.
42. Pedhazur E.J., Schmelkin L. Measurement, Design, and Analysis. 1991;Lawrence Erlbaum Associates, Hillsdale, NJ.
43. Ping R. Latent variable interaction and quadratic effect estimation: A two-step technique using structural equation analysis. Psychological Bulletin. 119:1996;166-175.
44. Ramoni M., Sebastiani P. Bayesian methods for intelligent data analysis. Bethold M., Hand D.J. An Introduction to Intelligent Data Analysis. 1999;129-166 Springer-Verlag, New York.
45. Reichenbach Hans. The Direction of Time. 1956;University of California Press, Berkeley.
46. Scheines R. An introduction to causal inference. McKim V.R., Turner P.T. Causality in Crisis? 1997;185-199 University of Notre Dame Press, Notre Dame, IN.
47. Scheines R. Estimating causal influences: TETRAD II model selection and Bayesian parameter estimation. Glymour C., Cooper G.F. Computation, Causation and Discovery. 1999;425-437 MIT Press, Menlo Park, CA.
48. Scheines R., Spirtes P., Glymour C., Meek C. TETRAD II: User's Manual. 1994;Lawrence Erlbaum, Hillsdale, NJ.
49. Schmenner R.W. The merit of making things fast. Sloan Management Review. 30(1):1988;11-17.
50. Sebastiani P., Ramoni M. Bayesian inference with missing data using bound and collapse. Journal of Computational and Graphical Statistics. 9(4):2000;779-800.
51. Shachter R.D. Evaluating influence diagrams. Operations Research. 34(6):1986;871-882.
52. Spearman C. General intelligence, objectively determined and measured. American Journal of Psychology. 15:1904;201-293.
53. Spiegelhalter D.J., Dawid A.P., Lauritzen S.L., Cowell R.G. Bayesian analysis with expert systems (with discussion). Statistical Science. 8:1993;219-283.
54. Spirtes P., Glymour C., Schienes R. Causation, Prediction, and Search. 1993;Springer-Verlag, New York.
55. Stelzl I. Changing causal relationships without changing the fit: Some rules for generating equivalent LISREL-models. Multivariate behavioral Research. 21:1986;309-331.
56. Suppes P. A Probabilistic Theory of Causality. 1970;North-Holland Publishing, Amsterdam.
57. Thurstone L.L. Vectors of the Mind. 1935;Chicago University Press, Chicago.
58. Vastag G., Whybark D.C. Global relations between inventory, manufacturing lead time and delivery date promises. International Journal of Production Economics. 30-31:1993;563-569.
59. Vastag G., Montabon F. Linkages among manufacturing concepts, inventories, delivery service and competitiveness. International Journal of Production Economics. 71:2001;195-204.
60. Verma T., Pearl J. Equivalence and synthesis of causal models. Proceedings, Sixth Workshop on Uncertainty in Artificial Intelligence. 1990;220-227 North Holland, Amsterdam.
61. West S.G., Finch J.F., Curran P.J. Structural equation models with nonnormal variables: Problems and remedies. Hoyle R.H. Structural Equation Modeling: Concepts, Issues and Applications. 1995;56-75 Sage, Thousand Oaks, CA.
62. Wright S. The method of path coefficients. Annals of Mathematical Statistics. 5:1934;161-215.
63. Whybark D.C., Vastag G. Global Manufacturing Practices: A Worldwide Survey of Practices and Manufacturing Planning and Control. 1993;Elsevier, Amsterdam.
64. Winkler R.L. The assessment of prior distributions in Bayesian analysis. Journal of American Statistical Association. 62:1967;776-800.