Analytical Problem Solving Based on Causal, Correlational and Deductive Models

References

• Ackoff, R. L., and Vergara, E. (1981), “Creativity in Problem Solving and Planning: A Review,” European Journal of Operational Research, 7, 1–13. DOI: 10.1016/0377-2217(81)90044-8.
   Web of Science ®Google Scholar
• Ahmadi-Javid, A., Jalali, A., and Klassen, K. (2017), “Outpatient Appointment Systems in Healthcare: A Review of Optimization Studies,” European Journal of Operational Research, 258, 3–34. DOI: 10.1016/j.ejor.2016.06.064.
   Web of Science ®Google Scholar
• Annis, D. H. (2005), “Rethinking the Paper Helicopter: Combining Statistical and Engineering Knowledge,” The American Statistician, 59, 320–326. DOI: 10.1198/000313005X70777.
   Web of Science ®Google Scholar
• Arora, S., and Barak, B. (2009), Computational Complexity: A Modern Approach, Cambridge: Cambridge University Press.
   Google Scholar
• Åström, K. J., and Hägglund, T. (2001), “The Future of PID Control,” Control Engineering Practice, 9, 1163–1175. DOI: 10.1016/S0967-0661(01)00062-4.
   Web of Science ®Google Scholar
• Baptiste, P., Le Pape, C., and Nuijten, W. (2001), Constraint-Based Scheduling: Applying Constraint Programming to Scheduling Problems, New York: Springer.
   Google Scholar
• Box, G. E. P., and Kramer, T. (1992), “Statistical Process Monitoring and Feedback Adjustment: A Discussion,” Technometrics, 34, 251–267. DOI: 10.2307/1270028.
   Web of Science ®Google Scholar
• Box, G. E. P., and Liu, P. Y. T. (1999), “Statistics as a Catalyst to Learning by Scientific Method, Part I – An Example,” Journal of Quality Technology, 31, 1–15. DOI: 10.1080/00224065.1999.11979889.
   Web of Science ®Google Scholar
• Box, G. E. P., and Draper, N. R. (1987), Empirical Model-Building and Response Surfaces, London: Wiley.
   Google Scholar
• Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994), Time Series Analysis, New Jersey: Prentice Hall.
   Google Scholar
• Breiman, L. (2001), “Statistical Modelling: The Two Cultures,” Statistical Science, 16, 199–231. DOI: 10.1214/ss/1009213726.
   Web of Science ®Google Scholar
• Carden, E. P., and Fanning, P. (2004), “Vibration Based Condition Monitoring: A Review,” Structural Health Monitoring, 3, 355–377. DOI: 10.1177/1475921704047500.
   Web of Science ®Google Scholar
• Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. R., and Wirth, R. (2000), CRISP-DM 1.0: Step-by-step Data Mining Guide, The CRISP-DM Consortium.
   Google Scholar
• Chittaro, L., and Ranon, R. (2004), “Hierarchical model-Based Diagnosis Based on Structural Abstraction,” Artificial Intelligence, 155, 147–182. DOI: 10.1016/j.artint.2003.06.003.
   Web of Science ®Google Scholar
• Davenport, T., and Harris, J. (2017), Competing on Analytics: The New Science of Winning (updated ed.), Boston, MA: Harvard Business School Press.
   Google Scholar
• De Mast, J. (2011), “The Tactical Use of Constraints and Structure in Diagnostic Problem Solving,” Omega, 39, 702–709. DOI: 10.1016/j.omega.2011.02.002.
   Web of Science ®Google Scholar
• De Mast, J. (2013), “Diagnostic Quality Problem Solving: A Conceptual Framework and Six Strategies,” Quality Management Journal, 20, 21–36.
   Google Scholar
• De Mast, J., and Kemper, B. P. H. (2013), “Principles of Exploratory Data Analysis: What Can We Learn From a Well-Known Case?,” Quality Engineering, 21, 366–375. DOI: 10.1080/08982110903188276.
   Web of Science ®Google Scholar
• De Mast, J., and Lokkerbol, J. (2012), “An Analysis of the Six Sigma DMAIC Method from the Perspective Of Problem Solving,” International Journal of Production Economics, 139, 604–614. DOI: 10.1016/j.ijpe.2012.05.035.
   Web of Science ®Google Scholar
• De Veaux, R. D., Hoerl, R. W., and Snee, R. D. (2016), “Big Data and the Missing Links,” Statistical Analysis and Data Mining, 9, 411–416. DOI: 10.1002/sam.11303.
   Web of Science ®Google Scholar
• Den Hertog, D., and Postek, K. (2016), “Bridging the Gap Between Predictive and Prescriptive Analytics—New Optimization Methodology Needed,” Research Report, Tilburg University.
   Google Scholar
• Derringer, G., and Suich, R. (1980), “Simultaneous Optimization of Several Response Variables,” Journal of Quality Technology,12, 214–219. DOI: 10.1080/00224065.1980.11980968.
   Web of Science ®Google Scholar
• Estrada-Flores, S., Merts, I., De Keterlaere, B., and Lammertyn, J. (2006), “Development and Validation of “Grey-Box” Models for Refrigeration Applications: A Review of Key Concepts,” International Journal of Refrigeration, 29, 931–946.
   Web of Science ®Google Scholar
• Godard, D., Laborie, P., and Nuijten, W. (2005), “Randomized Large Neighborhood Search for Cumulative Scheduling,” Proc. 15th International Conference on Automated Planning and Scheduling(ICAPS 05), pp. 81–89.
   Google Scholar
• Goos, P., and Jones, B. (2011), Optimal Design of Experiments: A Case Study Approach, London: Wiley.
   Google Scholar
• Hempel, C., and Oppenheim, P. (1948), “Studies in the Logic of Explanation,” Philosophy of Science, 15 135–175. DOI: 10.1086/286983.
   Google Scholar
• Hernan, M. A., Hsu, J., and Healy, B. (2019), “A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks,” Chance, 32, 42–49. DOI: 10.1080/09332480.2019.1579578.
   Google Scholar
• Hoerl, R. W., and Vining, G. G. (2020). “The Journey to Establish the Discipline of Statistical Engineering,” Applied Statistical Models in Business and Industry,1–12.
   Google Scholar
• Holland, P. W. (1986), “Statistics and Causal Inference,” Journal of the American Statistical Association, 81, 945–960. DOI: 10.1080/01621459.1986.10478354.
   Web of Science ®Google Scholar
• James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013), An Introduction to Statistical Learning, New York: Springer.
   Google Scholar
• Juran, J. M. (1998), “The Quality Improvement Process,” in Juran’s Quality Handbook (5th ed.), eds. J. M. Juran, A. B. Godfrey, New York: McGraw-Hill, pp. 51–74.
   Google Scholar
• Kackar, R. N. (1985), “Off-Line Quality Control, Parameter Design and the Taguchi Method” (with discussion), Journal of Quality Technology, 17, 176–209. DOI: 10.1080/00224065.1985.11978964.
   Web of Science ®Google Scholar
• Kerlinger, F. N., and Lee, H. B. (2000), Foundations of Behavioral Research (4th ed.). Fort Worth, TX: Harcourt College Publishers.
   Google Scholar
• Kitcher, P. (1998). “Explanation,” in Routledge Encyclopedia of Philosophy, ed. E. Craig, (vol. 3), London:Routledge.
   Google Scholar
• Korte, B., and Vygen, J. (2006), “Bin-Packing,” in Combinatorial Optimization: Theory and Algorithms (chap. 18), New York: Springer.
   Google Scholar
• Kuiper, A., Mandjes, M., De Mast, J., and Brokkelkamp, R. (in press), “A Flexible and Optimal Approach for Appointment Scheduling in Healthcare,” Decision Sciences Journal. DOI: 10.1111/deci.12517.
   Web of Science ®Google Scholar
• Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., Ost, D. E., Punjabi, N. M., Schatz, M., Smyth, A. R., and Stewart, P. W. (2019), “Control of Confounding and Reporting of Results in Causal Inference Studies,” Annals of the American Thoracic Society, 16, 22–28. DOI: 10.1513/AnnalsATS.201808-564PS.
   PubMed Web of Science ®Google Scholar
• Lee, C. C., and Lee, D. T. (1985), “A Simple On-Line Bin-Packing Algorithm,” Journal of the ACM, 32, 562–572. DOI: 10.1145/3828.3833.
   Web of Science ®Google Scholar
• Lepenioti, K., Bousdekis, A., Apostolou, D., and Mentzas, G. (2020), “Prescriptive Analytics: Literature Review and Research Challenges,” International Journal of Information Management, 50, 57–70. DOI: 10.1016/j.ijinfomgt.2019.04.003.
   Web of Science ®Google Scholar
• MacDuffie, J. P. (1997), “The Road to ‘Root Cause’: Shop-Floor Problem-Solving at Three Auto Assembly Plants,” Management Science, 43, 479–502. DOI: 10.1287/mnsc.43.4.479.
   Web of Science ®Google Scholar
• MacKay, R. J., and Oldford, R. W. (2000), “Scientific Method, Statistical Method, and the Speed of Light,” Statistical Science, 15, 254–278. DOI: 10.1214/ss/1009212817.
   Web of Science ®Google Scholar
• MacKay, R. J., and Steiner, S. H. (1997), “Strategies for Variability Reduction,” Quality Engineering, 10, 125–136. DOI: 10.1080/08982119708919115.
   Google Scholar
• Marksberry, P., Bustle, J., and Clevinger, J. (2011), “Problem Solving for Managers: A Mathematical Investigation of Toyota’s 8-Step Process,” Journal of Manufacturing Technology Management, 22, 837–852. DOI: 10.1108/17410381111160924.
   Google Scholar
• Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M. (2009), Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.), London: Wiley.
   Google Scholar
• Pearl, J. (2009), “Causal Inference in Statistics: An Overview,” Statistical Surveys, 3, 96–146.
   Google Scholar
• Pearl, J., and MacKenzie, D. (2018), The Book of Why—The New Science of Cause and Effect, New York: Basic Books.
   Google Scholar
• Rubin, D. (1974), “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies,” Journal of Educational Psychology, 66, 688–701. DOI: 10.1037/h0037350.
   Web of Science ®Google Scholar
• Rubin, D. (2005), “Causal Inference Using Potential Outcomes,” Journal of the American Statistical Association, 100, 322–331.
   Web of Science ®Google Scholar
• Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y. (2021), “Towards Causal Representation Learning,” Proceedings of the IEEE, 109, 612–634. DOI: 10.1109/JPROC.2021.3058954.
   Web of Science ®Google Scholar
• Shainin, R. (1993), “Strategies for Technical Problem Solving,” Quality Engineering, 5, 433–448. DOI: 10.1080/08982119308918984.
   Google Scholar
• Shmueli, G. (2010), “To Explain or to Predict?,” Statistical Science, 25, 289–310. DOI: 10.1214/10-STS330.
   Web of Science ®Google Scholar
• Simchi-Levi, D. (2014), “OM Research: From Problem-Driven to Data-Driven Research,” Manufacturing and Service Operations Management, 16, 2–10. DOI: 10.1287/msom.2013.0471.
   Web of Science ®Google Scholar
• Smith, G. F. (1988), “Towards a Heuristic Theory of Problem Structuring,” Management Science, 34, 1489–1506. DOI: 10.1287/mnsc.34.12.1489.
   Web of Science ®Google Scholar
• Smith, G. F. (1998), “Determining the Cause of Quality Problems: Lessons from Diagnostic Disciplines,” Quality Management Journal, 5, 24–41.
   Google Scholar
• Steiner, S. H., MacKay, R. J., and Ramberg, J. S. (2008), “An Overview of the Shainin System for Quality Improvement,” Quality Engineering, 20, 6–19. DOI: 10.1080/08982110701648125.
   Google Scholar
• Wolsey, L. A. (1998), Integer Programming, New York: Wiley.
   Google Scholar
• Woodward, J. (2019), “Scientific Explanation,” in The Stanford Encyclopedia of Philosophy (Winter 2019 ed.), ed. E. N. Zalta, Stanford, CA: Stanford University, available at https://plato.stanford.edu/archives/win2019/entries/scientific-explanation.
   Google Scholar
• World Economic Forum (2020), The Future of Jobs Report 2020, Geneva: WEF.
   Google Scholar