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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.

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Causal Inference

  • 章节

  • 发表日期：

    2025年

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  • Alex Coad

摘要

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.