Counterfactuals and Causal Inference
Counterfactuals and Causal Inference
Christopher Winship, Harvard University, Massachusetts
$34.99
USDDid 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 a butterfly ballot in some Florida
counties in the 2000 presidential election cost Al Gore votes? Simple cause-and-effect questions such as these are the motivation for much empirical work in the
social sciences. In this book, the counterfactual model of causality for observational
data analysis is presented, and methods for causal effect estimation are demonstrated using examples from sociology, political science, and economics.
- Causal inference from a counterfactual perspective
- Techniques for the estimation of causal effects
- Examples from sociology, political science, and economics
Reviews & endorsements
"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
Product details
December 2007Adobe eBook Reader
9780511346354
0 pages
0kg
30 tables
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- 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.
