Counterfactuals and causal inference : methods and principles for social research Stephen L. Morgan, Christopher Winship.
Material type: TextSeries: Analytical methods for social researchPublication details: Cambridge: Cambridge University Press, 2020.Edition: Second EditionDescription: xxiii, 499 pages : illustrations ; 26 cmISBN:- 9781107694163 (paperback)
- 300.72 M821C2 23
- H62 .M646 2015
- MAT029000
Item type | Current library | Collection | Call number | Status | Notes | Date due | Barcode | |
---|---|---|---|---|---|---|---|---|
Books | Central Library, IISER Bhopal Reference Section | Reference | 300.72 M821C2 (Browse shelf(Opens below)) | Not For Loan | Reserve | 11173 |
Browsing Central Library, IISER Bhopal shelves, Shelving location: Reference Section, Collection: Reference Close shelf browser (Hides shelf browser)
294.5921 D15V Vedas : | 300 M318I Identification problems in the social sciences | 300.1 Se7B Beyond reason : | 300.72 M821C2 Counterfactuals and causal inference : | 301 Ah51A Anthropology and ethnography are not equivalent : | 301.01 Ad7C Critical models : | 301.01 Ad7D Dialectic of enlightenment |
Revised edition of the authors' Counterfactuals and causal inference, published in 2007.
Includes bibliographical references (pages 451-496) and index.
Machine generated contents note: 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.
"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"--
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