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Causal Inference: The Mixtape
- 청구기호
- 300.72 CAU2021
- 발행사항
- New Haven, CT : Yale University Press, 2021
- 형태사항
- 572 p
- 서지주기
- Includes bibliographical references (p. 541-553) and index
- ISBN
- 9780300251685
- 분류기호
- 듀이십진분류법->300.72
소장정보
위치 | 등록번호 | 청구기호 / 출력 | 상태 | 반납예정일 |
---|---|---|---|---|
지금 이용 불가 (2) | ||||
한국노동연구원 | 00009839 | 대출중 | 2024.08.28 | |
한국노동연구원 | 00009838 | 대출중 | 2024.08.28 |
지금 이용 불가 (2)
- 등록번호
- 00009839
- 상태/반납예정일
- 대출중
- 2024.08.28
- 위치/청구기호(출력)
- 한국노동연구원
- 등록번호
- 00009838
- 상태/반납예정일
- 대출중
- 2024.08.28
- 위치/청구기호(출력)
- 한국노동연구원
책 소개
<DIV>An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences<BR />  <BR />“Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. <I>Causal Inference: The Mixtape</I> 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)<BR /><BR /> 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, 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.</DIV><br/><br/> <DIV>An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences</DIV><br/><br/>