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

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Victor S. Y. Lo , Dessislava A. Pachamanova ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.5334/dsj-2023-008

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. 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.

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Artificial Intelligence

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Causal inference Computer science Data science Analytics Business analytics Inference Predictive analytics Context (archaeology) Business intelligence Data analysis Management science Knowledge management Data mining Artificial intelligence Business model Business analysis Econometrics Paleontology Marketing Economics Biology Business

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Type: article
Language: en
Landing Page: https://doi.org/10.5334/dsj-2023-008
PDF: https://storage.googleapis.com/jnl-up-j-dsj-files/journals/1/articles/1435/6447b1212791c.pdf
OA Status: gold
Cited By: 3
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DOI: https://doi.org/10.5334/dsj-2023-008

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

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Language: en

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Publication year: 2023

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Publication date: 2023-01-01

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Authors: Victor S. Y. Lo, Dessislava A. Pachamanova

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Landing page: https://doi.org/10.5334/dsj-2023-008

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Concepts: Causal inference, Computer science, Data science, Analytics, Business analytics, Inference, Predictive analytics, Context (archaeology), Business intelligence, Data analysis, Management science, Knowledge management, Data mining, Artificial intelligence, Business model, Business analysis, Econometrics, Paleontology, Marketing, Economics, Biology, Business

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Cited by: 3

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Citations by year (recent): 2025: 2, 2024: 1

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