Counterfactuals and Causal Inference: Methods and Principles for Social Research
By: Morgan, Stephen L
| Winship, Christopher.
Material type: 
BookSeries: Analytical Methods for Social Research. Publisher: Cambridge et al., Cambridge University Press, 2010Description: 319 pages.ISBN: 978-0-521-67193-4.Subject(s): conterfactual approach | data analysis | social research | social science | estimation | methodsOnline resources: Publisher's website Summary: (2nd edition)
Description
Contents
Resources
Courses
About the Authors
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.
Examines causal inference from a counterfactual perspective
Offers techniques for the estimation of causal effects
Provides examples from the social, demographic, and health sciences
The second edition has been thoroughly revised and enlarged, and is 163% of the first edition by length
| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Monography | Library | C8 151 (Browse shelf) | Available | 00132565 |
(2nd edition)
Description
Contents
Resources
Courses
About the Authors
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.
Examines causal inference from a counterfactual perspective
Offers techniques for the estimation of causal effects
Provides examples from the social, demographic, and health sciences
The second edition has been thoroughly revised and enlarged, and is 163% of the first edition by length
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