Redeeming Data Science by Decision Modelling
This addresses the need for more rigorous and principled approaches in applied data science, though it appears incremental by building on existing Bayesian and business frameworks.
The paper tackles the problem of data science lacking foundational grounding by proposing Decision Modelling, which integrates machine learning with explicit value models, and demonstrates this through combining a model's ROC curve with a utility model.
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.