With the explosion of applications of Data Science, the field is has come loose from its foundations. This article argues for a new program of applied research in areas familiar to researchers in Bayesian methods in AI that are needed to ground the practice of Data Science by borrowing from AI techniques for model formulation that we term ``Decision Modelling.'' This article briefly reviews the formulation process as building a causal graphical model, then discusses the process in terms of six principles that comprise \emph{Decision Quality}, a framework from the popular business literature. We claim that any successful applied ML modelling effort must include these six principles. We explain how Decision Modelling combines a conventional machine learning model with an explicit value model. To give a specific example we show how this is done by integrating a model's ROC curve with a utility model.

翻译：随着数据科学应用的爆发式增长，该领域已逐渐脱离其理论基础。本文主张在贝叶斯方法在人工智能领域研究者所熟悉的学术视域内，开展一项新的应用研究计划——通过借鉴人工智能领域中的模型构建技术（我们称之为"决策建模"），为数据科学实践奠定基础。本文首先简要回顾了将建模过程视为构建因果图模型的方法，随后从构成"决策质量"框架（源自商业领域流行文献的六项原则）的角度展开讨论。我们主张，任何成功的应用机器学习建模工作都必须遵循这六项原则。本文阐释了决策建模如何将传统机器学习模型与显性价值模型相结合，并以具体案例展示了如何通过整合模型ROC曲线与效用模型来实现这一过程。