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Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research) (Englisch) Taschenbuch – 26. Oktober 2011

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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

Über das Produkt

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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Format: Taschenbuch Verifizierter Kauf
Excellent textbook for master and grad classes.
Transparent presentation of modern methods of causal inference using directed acyclic graphs.
Also a very stimulating book for current research practices.
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Amazon.com: HASH(0x999f45e8) von 5 Sternen 8 Rezensionen
44 von 45 Kunden fanden die folgende Rezension hilfreich
HASH(0x99e3ddd4) von 5 Sternen Excellent introduction to Causal Inference for social scientists 1. März 2010
Von Sociobabble - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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"). Additionally, the book contains a chapter on causal inference and repeated observations/longitudinal data. The book leaves aside issues of variance estimation using these approaches, presumably because of its more technical nature and the large amount of research activity currently in progress.

This book is not a research "cookbook" in the sense that it will provide code snippets illustrating each technique (or any code snippets at all for that matter), so you will be disappointed if that's what you are after. Its value is in providing a theoretically united and up-to-date review of causal inference in the social sciences (so you will actually know what you're talking about as compared to simply pasting code into Stata/SAS/R/whatever).

This book should be on the shelf of any self-respecting quantitative social scientist, and it will provide serious intellectual fodder for anyone interested in causal inference more generally.

[Disclosure: I know the author.]
19 von 21 Kunden fanden die folgende Rezension hilfreich
HASH(0x998e5528) von 5 Sternen Great place to start learning about causal inference 14. Oktober 2010
Von Cyrus Samii - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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.
13 von 14 Kunden fanden die folgende Rezension hilfreich
HASH(0x998e715c) von 5 Sternen Very very clear... 5. November 2010
Von LOV - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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.
HASH(0x998e5660) von 5 Sternen Outstanding book for someone who wants to learn causal inference 7. April 2013
Von W. YIP - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
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.
HASH(0x998e5c24) von 5 Sternen Strengths: multiple representations, PSM, IV 7. Juli 2015
Von hunter - Veröffentlicht auf Amazon.com
Format: Kindle Edition Verifizierter Kauf
Words, graphs, tables, and equations are used to convey the potential outcomes framework as applied to matching and regression estimators - really liked here chapters.

Discussion of panel methods was rushed and some tabular examples weren't transparent enough.

Great as a digital edition because you can bounce between equations, text, footnotes, etc.
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