
단행본
Causal Inference: The Mixtape
- 발행사항
- Yale University Press, 2021
- 형태사항
- p.571 ; 22cm
- 서지주기
- 서지(p.541-553)와 색인(p.561-572) 수록
소장정보
위치 | 등록번호 | 청구기호 / 출력 | 상태 | 반납예정일 |
---|---|---|---|---|
지금 이용 불가 (1) | ||||
자료실 | E207611 | 대출중 | 2024.07.02 |
지금 이용 불가 (1)
- 등록번호
- E207611
- 상태/반납예정일
- 대출중
- 2024.07.02
- 위치/청구기호(출력)
- 자료실
책 소개
An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences
“Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)
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.
“Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)
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.
An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences
목차
Acknowledgments
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
Conclusion
Bibliography
Permissions
Index