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Causal inference: the mixtape

By: Material type: TextTextPublication details: Yale University Press 2021 LondonDescription: x, 572 p. : ill. Includes bibliography and indexISBN:
  • 9780300251685
Subject(s): DDC classification:
  • 501 C8C2
Summary: Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages. https://yalebooks.yale.edu/book/9780300251685/causal-inference
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Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 27-B / Slot 1326 (0 Floor, East Wing) Non-fiction General Stacks 501 C8C2 (Browse shelf(Opens below)) Checked out 12/11/2025 204579

Table of contents

Welcome
1 Introduction
2 Probability and Regression Review
3 Directed Acyclic Graphs
4 Potential Outcomes Causal Model
5 Matching and Subclassification
6 Regression Discontinuity
7 Instrumental Variables
8 Panel Data
9 Difference-in-Differences
10 Synthetic Control
11 Conclusion
Teaching Resources

Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.

https://yalebooks.yale.edu/book/9780300251685/causal-inference

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