Analytical Problem Solving Based on Causal, Correlational and Deductive Models

Jeroen de Mast (Corresponding author), Stefan Steiner, Wim P.M. Nuijten, Daniel Kapitan

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
139 Downloads (Pure)

Abstract

Many approaches for solving problems in business and industry are based on analytics and statistical modeling. Analytical problem solving is driven by the modeling of relationships between dependent (Y) and independent (X) variables, and we discuss three frameworks for modeling such relationships: cause-and-effect modeling, popular in applied statistics and beyond, correlational predictive modeling, popular in machine learning, and deductive (first-principles) modeling, popular in business analytics and operations research. We aim to explain the differences between these types of models, and flesh out the implications of these differences for study design, for discovering potential X/Y relationships, and for the types of solution patterns that each type of modeling could support. We use our account to clarify the popular descriptive-diagnostic-predictive-prescriptive analytics framework, but extend it to offer a more complete model of the process of analytical problem solving, reflecting the essential differences between causal, correlational, and deductive models.
Original languageEnglish
Pages (from-to)51-61
Number of pages11
JournalAmerican Statistician
Volume77
Issue number1
Early online date10 Mar 2022
DOIs
Publication statusPublished - Jan 2023

Keywords

  • Business analytics
  • Causal inference
  • Machine learning
  • Operations research
  • Predictive modeling
  • Prescriptive analytics
  • Problem solving

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