TY  - BOOK
AU  - Morgan,Stephen L.
AU  - Winship,Christopher
TI  - Counterfactuals and causal inference: methods and principles for social research 
T2  - Analytical methods for social research
SN  - 9781107694163 (paperback)
AV  - H62 .M646 2015
U1  - 300.72 M821C2 23
PY  - 2020///
CY  - Cambridge
PB  - Cambridge University Press
KW  - Social sciences
KW  - Research
KW  - Methodology
KW  - Causation
KW  - MATHEMATICS / Probability & Statistics / General
KW  - bisacsh
N1  - 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
N2  - "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"--
UR  - http://assets.cambridge.org/97811070/65079/cover/9781107065079.jpg
ER  -