Counterfactuals and causal inference methods and principles for social research
| Main Authors: | , |
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| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Language Notes: | English. |
| Published: |
New York, NY :
Cambridge University Press,
2015
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| Edition: | Second edition. |
| Series: | Analytical methods for social research.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of 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