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Causality [Englisch] [Gebundene Ausgabe]

Judea Pearl

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14. September 2009
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.

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Causality + Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning)
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'Make no mistake about it: this is an important book … The field has no shortage of lively controversy and divergent opinion, but be that as it may, this is certainly one of the contributions that will bring this material further out of the closet and into the face of the broader statistical community, a move that we should welcome both as consumers and as testers of its utility.' Journal of the American Statistical Association

'Pearl's career has been motivated by problems of artificial intelligence, but the implications of this book are much broader. The distinctions he raises and the mathematical foundation he assembles are critical for every field of scientific endeavor. This updated edition of a modern classic deserves a broad and attentive audience.' H. Van Dyke Parunak,

Über das Produkt

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, and cognitive science.

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In diesem Buch (Mehr dazu)
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Buchdeckel | Copyright | Inhaltsverzeichnis | Auszug | Stichwortverzeichnis
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Die hilfreichsten Kundenrezensionen auf (beta) 3.9 von 5 Sternen  16 Rezensionen
71 von 73 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Read this book; here's what you need to know 30. Januar 2011
Von Samuel W. Mitchell - Veröffentlicht auf
Format:Gebundene Ausgabe|Verifizierter Kauf
If you are at all capable of understanding it, you must read this book. It gives a general, and theoretical, overview of a highly promising and quite technical theory of what causes are and how to use them in experiments and reasoning. This is applied to practical examples in a very wide range of fields. This is a major step forward in understanding causal reasoning specifically, and scientific reasoning generally.

If you haven't read the first edition:
First, read the Epilogue. Don't start at the beginning. The epilogue will tell you why you should read the book. The book is technical. It is more than worth the effort to follow it.
To follow the mathematics you need a thorough grip on basic probability theory. That is, reasoning using conditional probabilities, conjunctions, independent variables, confounding variables - that sort of thing. You also need the basics of graph theory. You really need to be comfortable with these. The reasoning is very sophisticated, even though the mathematics is basic. It is helpful (but not essential) to know the following too: symbolic logic, basic statistics, some Macroeconomics, some computer science and (occasionally) a little vector algebra.
If you have basic probability and know what a graph is, you ought to read the book.

If you read the first edition:
The second edition repeats the first edition verbatim, but at the end of most chapters there's a clearly defined section dealing with subsequent developments. There's a long chapter at the end that updates you on the replies to the first edition, and some helpful new material explaining things (like d-separation) that were tricky the first time through. Some of this is on the author's website too. The updates are concise. Replies to philosophers (at least) are ultimately devastating, although Pearl could explain himself more fully.
I am a philosopher of science.
35 von 37 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen some interesting questions 11. Februar 2010
Von Steve - Veröffentlicht auf
Format:Gebundene Ausgabe|Verifizierter Kauf
In the introductory material, the book claims the graphical method presented in this book 'solves' the problem of causality. However, the book does not read as if the problem has been solved. Instead, it reads like an extended discussion/argument with philosophers, scientists, and statisticians. The book raises a great many interesting questions (some it raises only implicitly), so for this reason I give it 5 stars without hesitation. I do recommend, though, that the third edition of this book substantially reorganize the material; for example, the excellent epilogue should be brought forward as introductory material (and expanded).
29 von 33 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Causality 5. Dezember 2009
Von José-Fernando PIneda - Veröffentlicht auf
Format:Gebundene Ausgabe|Verifizierter Kauf
This is a very suggestive analysis on a quite forgotten by now subject: the study of causality in the social sciences. The author traces very much the original idea of Havelmmo on the nature of econometrics, and brings up to date in the study of several strands of social phenomena that have to do with the nature of causation in human behaviour. He makes use of the notions of bayesian statistics, probability theory, graph theory, correlation analysis and the otherwise called non recursive hierarchical models in social studies. Recommended to those persons who still believe one of the purposes of social studies is to identify and measure causal chains and mechanisms and not simply to focus on correlations and forecasting techniques without due regard to the notion of what causes what and how does it seem to operate in reality.
50 von 61 Kunden fanden die folgende Rezension hilfreich
2.0 von 5 Sternen Very difficult to absorb. Not the best learning tool. 14. September 2011
Von Gaetan Lion - Veröffentlicht auf
Format:Gebundene Ausgabe|Verifizierter Kauf
This book is not for the layperson. It is not even for most autodidact mathematicians. Unless you have a degree in mathematics or you are a professional using advanced mathematics in your daily working life, you probably will experience much frustration reading this book. Reasonably advanced Probability Theory and Bayesian Statistics are two domains that may be extremely helpful in deciphering this book. Without them, I would recommend passing on this one.

For one thing, Judea Pearl frequently uses different math notation descriptions than the ones you are familiar with for such concepts as correlation, covariance, and linear regression among others. Pearl even turns on their heads simple concepts such as "y" stands for the dependent variable and "x" for an independent variable (he treats x very often as the dependent variable; and y sometimes as an independent one). Those obfuscations related to foundational concepts make it difficult for the reader to build knowledge related to Pearl's far more complicated methods.

None of the above detracts from the pioneering quality of Pearl's work on causality.

The above just gives the prospective reader a fair warning whether he is equipped and motivated to tackle such a challenging book. Also, the material could have been presented in a more user friendly way to increase the audience to at least the ones with reasonably good quantitative numeracy without being professional mathematicians.
8 von 8 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen The most important book on the subject, but not necessarily the best introduction 28. April 2013
Von B. Paulewicz - Veröffentlicht auf
Format:Kindle Edition|Verifizierter Kauf
I am a cognitive psychologist with some modest background in statistics and so I will only say something about the importance of this book to people like me. In psychology, as in many other sciences where a) important causal relations often cannot be tested directly by means of experimental manipulation or b) the validity of experimental manipulation or of the effects measures is often questionable it is essential to understand and use the ideas presented in this groundbreaking book. For example, whenever you perform an experiment there are essentially only a few ways in which your manipulation or your effect's measures can be problematic (with regard to the research question). Knowing exactly how this can happen allows you to find the problem quicker or, even better, find it in advance. In fact, many published experiments are simply attempts to address this kind of issues even though it would probably come as a surprise to the authors of these studies to see that it is the case. Also, in certain areas of psychology, e.g., individual differences or clinical psychology, heavy use is made of mediational analyses, structural models and various almost-but-not-quite experimental designs. One of the shocking and inescapable implications of Pearl's discoveries is that a lot of the conclusions routinely drawn from such studies are simply wrong, for example, the typical way of doing mediational analysis (be it vanilla Baron-Kenny, it's trivial extension to nonlinear settings or Baron-Kenny + bootstrap to compute certain confidence intervals) assumes that the mediator is measured without error but in psychology it is almost allways measured with substantial error - causal analysis let's you discover how exactly this affects the validity of the conclusions. I wont even try to begin to explain in what ways the structural equation models are abused in the majority of papers I've seen. If your are a psychologist than I suppose this might not be the best place to start - I'd recommend going through "Causal inference in statistics" (available from Judea Pearl's website) several times before reading this book.
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