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Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research) 1st Edition

4.9 out of 5 stars 9 customer reviews
ISBN-13: 978-0521671934
ISBN-10: 0521671930
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Editorial Reviews

Review

"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

Book 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 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.
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Product Details

  • Series: Analytical Methods for Social Research
  • Paperback: 328 pages
  • Publisher: Cambridge University Press; 1 edition (July 30, 2007)
  • Language: English
  • ISBN-10: 0521671930
  • ISBN-13: 978-0521671934
  • Product Dimensions: 6 x 0.8 x 9 inches
  • Shipping Weight: 1.3 pounds (View shipping rates and policies)
  • Average Customer Review: 4.9 out of 5 stars  See all reviews (9 customer reviews)
  • Amazon Best Sellers Rank: #408,252 in Books (See Top 100 in Books)

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This book is an excellent and relatively non-technical review of causal inference in the social sciences. The authors condense a huge literature that spans economics, statistics, sociology, philosophy, medical statistics, and computer science into manageable pieces appropriate for scholars and graduate students in the social sciences.

The authors' primary contribution is linking the work on causal inference in diverse fields together, presenting a theoretically coherent view of causal inference that draws extensively on Judea Pearl's work in philosophy and machine learning (see his book Causality: Models, Reasoning and Inference). The authors successfully illuminate the equations underlying the work of Paul Rosenbaum, Donald Rubin, Charles Manski, James Heckman, Joshua Angrist, Guido Imbens, James Robins, and Paul Holland (along with many others) by connecting them to Pearl's fundamentally graphical view of causal thinking. The authors allow readers to grasp such a broad selection of research by presenting each element as a natural extension of an overarching theoretical perspective.

The book covers the strengths and weaknesses of many popular quasi-experimental approaches to causal inference, including conditioning (aka "controlling for other variables"), instrumental variables/natural experiments, case-to-case matching, propensity score matching, propensity score blocking, and propensity score weighting. It also presents a great overview of Charles Manski's work on minimal identification approaches (i.e., "let's see what the data can tell us if we invoke as few assumptions as possible").
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Format: Paperback
I have many colleagues who say that they don't like this book because it's a mishmash of different analytical approaches and because it's treatment is incomplete. But I disagree with them: the book is a terrific introduction to the current literature on causal inference in observational studies. It provides the core intuitions needed to understand why randomization matters and how methods like matching, instrumental variables, differences in differences, regression discontinuity, etc try to overcome the problem of non-random treatment assignment. It also provides a soft introduction to analysis via potential outcomes and directed acyclic graphs. By being eclectic rather than purist in applying these analytical approaches, I think it better prepares the student for wading through the current literature, which is divided into camps who favor one or the other approach. It does not arm the student with enough knowledge to develop their own estimators or to handle questions of inference after you've achieved "identification", but then having it do so would be to ask too much of an introductory textbook. The bibliography is also great.

This textbook is the perfect thing for graduate students in the social sciences, public health, and education to read in their first semester of graduate school, along with starting on the more traditional methodological, statistical, or econometric texts. For any social scientist that currently feels "out of touch" with the causal inference literature, reading this book will bring you up to speed, at least in terms of intuitions, very quickly.
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Causal inference is not an easy topic for newcomers and even for those who have advanced education and deep experience in analytics or statistics. I have read many of causal inference books and this is, I would say, is the clearest one. It would repeatedly demonstrate the techniques with numerical examples unless you are completely convinced. Perhaps because it's written for social scientists, it is so clear (and sometimes feeling too clear) that I sometimes have to skip a lot. Apart from its hard-to-beat clarity, I would also praise the authors for bringing in Judea Pearl's causal diagrams along with Don Rubin's potential outcome approach (Rubin Causal Model) - two super ideas that have different ways to solving the same type of problems. This book should be one of the standard texts in the causal inference literature, along with Rosenbaum's, Pearl's, and Rubin's books.
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I have read this book multiple times. Every time I read it, I learn something new. I agree entirely with all the reviews so far on this book. The strength of this book is that it gives you detail rationales on how causal inference can be made based on the potential outcome model. It certainly ties the potential outcome model well with Pearl's causal DAG. Most causal inference course materials dive quickly into details and I quickly lost sight of the overall picture. This book provides me with all the intricate missing arguments that professors thought were trivial and do not need to waste the time to explain. The best part is that this book does not contain any R or SAS code as in most statistics textbooks. All the examples can be done with simple arithmetic. It demonstrates that causal inference is about applying a new mind-set to think about the problem instead of running endless and mindless regression models on the computer.
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