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Causality: Models, Reasoning and Inference 2nd Edition
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- ISBN-10052189560X
- ISBN-13978-0521895606
- Edition2nd
- PublisherCambridge University Press
- Publication dateSeptember 14, 2009
- LanguageEnglish
- Dimensions7.5 x 1.5 x 11 inches
- Print length484 pages
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Editorial Reviews
Review
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, Computing Reviews
"Pearl’s book is about probabilistic approaches to causality and it gives, especially, empirical researchers working with observational data an immense aid to their research. It also gives theoretical statisticians something to think about as it raises many issues of estimation for example in respective data generating processes. ... This work of Pearl’s is an invaluable contribution to the current discussion on the topic of causal modeling. As described by the author his main objective of the book is to develop a framework that integrates substantive knowledge including counterfactuals (through new notations and concepts) with statistical data so as to refine the former and to interpret the latter."
Priyantha Wijayatunga, Significance
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- Publisher : Cambridge University Press; 2nd edition (September 14, 2009)
- Language : English
- Hardcover : 484 pages
- ISBN-10 : 052189560X
- ISBN-13 : 978-0521895606
- Item Weight : 2.27 pounds
- Dimensions : 7.5 x 1.5 x 11 inches
- Best Sellers Rank: #67,017 in Books (See Top 100 in Books)
- #14 in Epistemology Philosophy
- #59 in Probability & Statistics (Books)
- #182 in History & Philosophy of Science (Books)
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In these eleven chapters followed by an epilogue, Dr. Pearl's manuscript postulates representational and computational foundation for the processing of information under uncertainty. It commences with introduction of simpler concepts in Bayesian inference, causality and corresponding proves. However, as text progresses into causal vs. statistical concepts along with theory of inferred causation, the theorems get arduous, somewhat counter-intuitive and the text becomes demanding to keep up. Chapter 3 is an interesting read where causality is discussed in context of philosophy and history. As Dr. Liu states, Judea Pearl's thesis regarding statistics that it deals with quantitative constructs like mean, variance, correlation, regression, dependence, conditional independence, association, likelihood, collapsibility, risk ratio, odd ratio, marginalization, conditionalization, etc. Meanwhile the causal analysis deals with the topics of randomization, influence, effect, confounding, disturbance, correlation, intervention, explanation and attribution. One of the challenges while following Dr. Pearl's work is that it abstracts causation discussing it in mathematical and philosophical manner without providing concrete mathematical and computational model for applied research. I believe the book provides great foundation for formal representation of causal analysis and its components, such as do(x) to represent intervention. Automated Reasoning Group at UCLA has made some strides in this area however the applied research aspects of this formalism still needs to be `tightly bound' by reason of scarcity of empirical evidence for the algorithms in practice.
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Pearl is essential.
However the book is unfortunately way too verbose. The reader probably doesn’t need a lot of persuasion about the value of causal inference as indicated by the steep price they have paid to buy the book. Still arguments like the stability of causal relationships and the merits of the functional representation are repeated over and over and over for pages, testing my patience as a reader.













