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.

摘要

许多解决商业和工业问题的方法都基于分析和统计建模。分析问题的解决是由因变量 (Y) 和自变量 (X) 之间的关系建模驱动的，我们讨论了对这种关系建模的三个框架：因果建模，在应用统计及其他领域很流行，相关预测建模，在机器学习和演绎（第一性原理）建模中很流行，在业务分析和运筹学中很流行。我们旨在解释这些模型类型之间的差异，并充实这些差异对研究设计、发现潜在 X/Y 关系以及每种建模类型可以支持的解决方案模式类型的影响。