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단행본Analytical methods for social research

Counterfactuals and causal inference: methods and principles for social research

서명/저자사항
Counterfactuals and causal inference : methods and principles for social research
개인저자
Morgan, Stephen L 1971- | Winship, Christopher
발행사항
New York, NY : Cambridge University Press, 2015.
형태사항
xxiv, 499 p. ; 25 cm.
총서사항
Analytical methods for social research
ISBN
9781107694163
주기사항
Revised edition of the authors' Counterfactuals and causal inference, published in 2007 Includes bibliographical references and index
주제어
Social sciences, Methodology. Social sciences, Research. MATHEMATICS / Probability & Statistics / General
소장정보소개관련정보
소장정보
위치등록번호청구기호 / 출력상태반납예정일
이용 가능 (1)
자료실WM021108대출가능-
이용 가능 (1)
  • 등록번호
    WM021108
    상태/반납예정일
    대출가능
    -
    위치/청구기호(출력)
    자료실
책 소개
In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.

This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields.

목차

Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction; Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model; 3. Causal graphs; Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators; 5. Matching estimators of causal effects; 6. Regression estimators of causal effects; 7. Weighted regression estimators of causal effects; Part IV. Estimating Causal Effects When Backdoor Conditioning Is Ineffective: 8. Self-selection, heterogeneity, and causal graphs; 9. Instrumental-variable estimators of causal effects; 10. Mechanisms and causal explanation; 11. Repeated observations and the estimation of causal effects; Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis; Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science.