From Meaningful Data Science to Impactful Decisions: The Importance of Being Causally Prescriptive

摘要

© 2023 The Author(s).This article proposes a framework for transition from traditional data science, where the focus is on extracting value from available data, to goal-driven analytical decision-making, where the business objective is defined first, through integration of various analytical techniques in a common setting. We discuss the link between predictive analytics and prescriptive analytics in the context of formulating the problem and assert that all prescriptive analytics problem formulations assume a causal link between decisions and outcomes. We emphasize the role of predictive analytics and causal inference in specifying the causal link between decisions and outcomes accurately and ultimately in aligning the analysis with the business objectives. We offer practical examples that integrate various required analytics tasks and describe scenarios where causal inference is required versus not required.