Causal Inference

Alex Coad ()

Chapter Chapter 12 in Data Science MBA, 2025, pp 153-163 from Springer

Abstract: Abstract This chapter emphasizes the importance of causal inference in data science, distinguishing between correlation and causation. It introduces Directed Acyclic Graphs (DAGs) as a tool for visualizing causal relationships and discusses techniques for causal inference, including Randomized Controlled Trials (RCTs), natural experiments, Regression Discontinuity Design (RDD), and Instrumental Variables (IV). The chapter underscores the need for causal understanding to make informed decisions, and highlights the limitations of observational data in establishing causality.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sptchp:978-981-95-2433-4_12

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DOI: 10.1007/978-981-95-2433-4_12

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