In this book, the essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics. 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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"This book is the first representative of a growing surge of interest among social scientists and economists to reclaim their professions from the tyrany of regression analysis and address cause-effect relationships squarely and formally. The book is unique in recognizing the equivalence between the counterfactual and graphical approaches to causal analysis and shows readers how to best utilize the distinct features of each. An indispensible reading for every forward-looking student of quantitative social science." -Judea Pearl University of California, Los Angeles
"...Morgan and Winship have written an important, wide-ranging, careful, and original introduction to the modern literature on causal inference in nonexperimental social research."
Canadian Journal of Sociology
Stephen L. Morgan is Associate Professor of Sociology and the current Director of the Center for the Study of Inequality at Cornell University. His previous publications include On the Edge of Commitment: Educational Attainment and Race in the United States (2005).
Christopher Winship is Diker-Tishman Professor of Sociology at Harvard University. For the past twelve years he has served as editor of Sociological Methods and Research. He has published widely in a variety of journals and edited volumes.
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Descripción Cambridge University Press, 2007. Estado de conservación: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: Part I. Counterfactual Causality and Empirical Research in the Social Sciences: 1. Introduction; 2. The counterfactual model; Part II. Estimating Causal Effects by Conditioning: 3. Causal graphs, identification, and models of causal exposure; 4. Matching estimators of causal effects; 5. Regression estimators of causal effects; Part III. Estimating Causal Effects When Simple Conditioning is Ineffective: 6. Identification in the absence of a complete model of causal exposure; 7. Natural experiments and instrumental variables; 8. Mechanisms and causal explanation; 9. Repeated observations and the estimation of causal effects; Part IV. Conclusions: 10. Counterfactual causality and future empirical research in the social sciences. Nº de ref. de la librería ABE_book_new_0521671930
Descripción Cambridge University Press, 2007. Paperback. Estado de conservación: New. book. Nº de ref. de la librería 0521671930
Descripción Cambridge University Press, 2007. Paperback. Estado de conservación: New. 1. Nº de ref. de la librería DADAX0521671930
Descripción Cambridge Univ Pr, 2007. Paperback. Estado de conservación: Brand New. 1st edition. 319 pages. 8.75x6.00x0.75 inches. In Stock. Nº de ref. de la librería 0521671930
Descripción Cambridge University Press, 2007. Paperback. Estado de conservación: New. Nº de ref. de la librería P110521671930
Descripción Estado de conservación: Brand New. Book Condition: Brand New. Nº de ref. de la librería 97805216719341.0