Stephen L. Morgan

Counterfactuals and Causal Inference (eBook, PDF)

Methods and Principles for Social Research

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 17. November 2014
• Englisch
• ISBN-13: 9781316164440
• Artikelnr.: 41865478

Autorenporträt

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).

Inhaltsangabe

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.