Counterfactuals and Causal Inference

Produktbeschreibung

This new edition aims to convince social scientists to take a counterfactual approach to the core questions of their fields. 

Inhaltsverzeichnis

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. 

Autoreninfo

Stephen L. Morgan is the Bloomberg Distinguished Professor of Sociology and Education at Johns Hopkins University. He was previously the Jan Rock Zubrow '77 Professor in the Social Sciences and the director of the Center for the Study of Inequality at Cornell University. His current areas of interest include social stratification, the sociology of education, and quantitative methodology. He has published On the Edge of Commitment: Educational Attainment and Race in the United States (2005) and, as editor, the Handbook of Causal Analysis for Social Research (2013).