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Counterfactuals and causal inference : methods and principles for social research / Stephen L. Morgan and Christopher Winship.

By: Contributor(s): Series: Analytical methods for social researchPublisher: New York : Cambridge University Press, 2015Edition: 2nd edDescription: xxiii, 499 p. : ill. ; 26 cmISBN:
  • 9781107065079
  • 9781107694163
Subject(s): DDC classification:
  • 300.72 MOR
Contents:
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.
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BOOKs . General Stacks 300.72 MOR (Browse shelf(Opens below)) PB Available 36063

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.