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Counterfactuals and causal inference: methods and principles for social research/
"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...
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| Format: | Printed Book |
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New York:
Cambridge University Press,
2015.
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| Edition: | 2nd ed. |
| Series: | Analytical methods for social research
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| Online Access: | https://books.google.co.in/books?id=Q6YaBQAAQBAJ&printsec=frontcover&dq=Counterfactuals+and+causal+inference+by+Stephen+L+Morgan&hl=en&sa=X&ved=0ahUKEwi9rMWm9PXQAhUcTY8KHWkQCcgQ6AEIGzAA#v=onepage&q=Counterfactuals%20and%20causal%20inference%20by%20Stephen%20L%20Morgan&f=false |
Table of Contents:
- 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.