Counterfactuals and Causal Inference

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Cambridge University Press, 2015 - Mathematics - 499 pages
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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Counterfactuals and the Potential Outcome Model
Causal Graphs
Estimating Causal Effects by Conditioning on Observed Variables to Block
Matching Estimators of Causal Effects
Regression Estimators of Causal Effects
Weighted Regression Estimators of Causal Effects
SelfSelection Heterogeneity and Causal Graphs
Instrumental Variable Estimators of Causal Effects
Mechanisms and Causal Explanation
Repeated Observations and the Estimation of Causal Effects
Estimation When Causal Effects Are Not PointIdentified by Observables
Counterfactuals and the Future of Empirical Research in Observational

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About the author (2015)

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).

Christopher Winship is the Diker-Tishman Professor of Sociology and member of the senior faculty of Harvard's Kennedy School of Government. Prior to coming to Harvard in 1992, he was Professor of Sociology and Statistics and by courtesy Economics at Northwestern University. His research focuses on statistical models for causal inference - most recently mechanisms and endogenous selection; how black clergy in Boston have worked with police to reduce youth violence; the effects of education on mental ability; pragmatism as the basis for a theory of action; the implications of advances in cognitive psychology for sociology; sociological approaches to how individuals understand justice. Since 1995 he has been editor of Sociological Methods and Research.

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