Counterfactuals and Causal Inference
Methods and Principles for Social Research
Published on 30. July 2007
Book
Paperback/Softback
328 pages
978-0-521-67193-4 (ISBN)
Description
Did mandatory busing programs in the 1970s increase the school achievement of disadvantaged minority youth? Does obtaining a college degree increase an individual's labor market earnings? Did the use of the butterfly ballot in some Florida counties in the 2000 presidential election cost Al Gore votes? If so, was the number of miscast votes sufficiently large to have altered the election outcome? At their core, these types of questions are simple cause-and-effect questions. Simple cause-and-effect questions are the motivation for much empirical work in the social sciences. This book presents a model and set of methods for causal effect estimation that social scientists can use to address causal questions such as these. The essential features of the counterfactual model of causality for observational data analysis are presented with examples from sociology, political science, and economics.
Reviews / Votes
"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
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Product notice
Paperback (trade)
Illustrations
30 Tables, unspecified
Dimensions
Height: 227 mm
Width: 150 mm
Thickness: 20 mm
Weight
450 gr
ISBN-13
978-0-521-67193-4 (9780521671934)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Stephen L. Morgan | Christopher Winship
Counterfactuals and Causal Inference
Methods and Principles for Social Research
Book
11/2014
2nd Edition
Cambridge University Press
€45.00
Available immediately
Additional editions
Stephen L. Morgan | Christopher Winship
Counterfactuals and Causal Inference
Methods and Principles for Social Research
Book
11/2014
2nd Edition
Cambridge University Press
€119.50
Shipment within 15-20 days
Stephen L. Morgan | Christopher Winship
Counterfactuals and Causal Inference
Methods and Principles for Social Research
E-Book
12/2007
1st Edition
Cambridge University Press
€32.49
Available for download
Persons
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.
Content
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.