Rating: 
Summary: Covariation and causality: when does one imply the other?
Review: The ideal reader for this remarkable book has some background in mathematical statistics and/or possesses very strong quantitative skills to follow Judea Pearl's mathematical solution to the enduring philosophical problem of how do we really know that "X" is a cause of "Y"? Is it ever valid to infer causality from knowledge of the mere association of variables (without experimental controls)? Some of the topics covered in the books's 10 chapters include: probability theory, Bayesian networks, Markov models, Structural Equation Modeling, Simpson's Paradox, and Counterfactuals. Pearl's presentation of the numerous mathematical formulas on which his argument rests is clear and direct. As a bonus, Pearl wisely includes an Epilogue containing a lecture he gave in 1996 entitled, "The Art and Science of Cause and Effect." This twenty-seven page essay is a thought-provoking, highly readable treatment of the history of the problem of causality. In my view, the Epilogue itself justifies the price of the book. Pearl concludes it by comparing his theory of causality to the first mathematical tool,the abacus: "But the really challenging problems are still ahead: We still do not have a causal understanding of poverty and cancer and intolerance, and only the accumulation of data and the insight of great minds will eventually lead to such understanding. "The data is all over the place, the insight is yours, and now an abacus is at your disposal, too. I hope the combination amplifies each of these components."
Rating:
Summary: Important but difficult
Review: The scientific research community has adopted rigorous methods to eliminate the need for subjective judgments about many things, but when it comes to testing whether X causes Y, they revert to intuition and hand-waving. This book makes a strong argument that we shouldn't accept that. It demonstrates that it is possible to turn intuitions about causation into hypotheses that are unambiguous and testable.
But the style is sufficiently dense and dry we will need some additional books with more practical styles before these ideas become widely understood. The style is fairly good by the standards of books whose main goal is rigorous proof, but it's still hard work to learn a large number of new concepts that are mostly referred to by terse symbols whose meaning can't be found via a glossary or index. Pearl occasionally introduces a memorable word, such as do(x), the way a software engineer who wants readable code would, but mostly sticks to single-character symbols that seem unreasonably hard (at least for us programmers who are used to descriptive names) to remember.
If you're uncertain whether reading this book is worth the effort, I strongly recommend reading the afterword first. It ought to have been used as the introduction, and without it many readers will be left wondering why they should believe they will be rewarded for slogging through so much dry material.
Rating: 
Summary: Wishful Reasoning
Review: This book is fundamentally defective. Now the author stepped in generic causation activities stepping back from the obstinate stalwarts of empirical causation. But still he is missing the point of the matter. There is no geater illusion to human thought than thinking in terms of linear causality even embellished with probabilty and graph theories additions. Apart from the innate symmetricity of structural equation systems, the very definition for the conditional probability as well as Bayes's law of inverse probability unambiguously suggest the reversibility of cause-effect relationships. Such crucial property is the heart of the real world processes, because all of them from physical effects to biological aging to socio-economic phenomena are eventually liable to causal reversal. For car-minded readers it may be well to remember how can work the transmission power train. It is time to stop spawning false beliefs, for any theory on causality, mathematical, physical or philosophical, falling short the reversible causal mechanisms is substantially fallacious, however sophisticated it may be, and it deeply distorts our understanding of the world. So, dear reader, wait other decade when the author will grow up to the evident fact that processes run backwards, that the direction of causality turns the other way round, and that the linear causal ordering was only the desease of the mental growth. Then we eventually get the rightened but completely contrary title:"Reversible Causality: Models, Reasoning, Inference". And it will start with the historical recognition: "A is a cause of B only when B can cause A". Amen.
Rating:
Summary: another myth on causality
Review: This book is fundamentally defective. Now the author stepped in generic causation activities stepping back from the obstinate stalwarts of empirical causation. But still he is missing the point of the matter. There is no geater illusion to human thought than thinking in terms of linear causality even embellished with probabilty and graph theories additions. Apart from the innate symmetricity of structural equation systems, the very definition for the conditional probability as well as Bayes's law of inverse probability unambiguously suggest the reversibility of cause-effect relationships. Such crucial property is the heart of the real world processes, because all of them from physical effects to biological aging to socio-economic phenomena are eventually liable to causal reversal. For car-minded readers it may be well to remember how can work the transmission power train. It is time to stop spawning false beliefs, for any theory on causality, mathematical, physical or philosophical, falling short the reversible causal mechanisms is substantially fallacious, however sophisticated it may be, and it deeply distorts our understanding of the world. So, dear reader, wait other decade when the author will grow up to the evident fact that processes run backwards, that the direction of causality turns the other way round, and that the linear causal ordering was only the desease of the mental growth. Then we eventually get the rightened but completely contrary title:"Reversible Causality: Models, Reasoning, Inference". And it will start with the historical recognition: "A is a cause of B only when B can cause A". Amen.
Rating:
Summary: A Pioneering Book on Causality
Review: This is a pioneering book dealing exhaustively with the subject of causation. Its contribution to the field of "Uncertainty in AI" is unmeasureable. It dealt with graphical models for reasoning in depth. For computer scientists looking for an encyclopedia of algorithms and applications on causation, there can not be a better book. I highly recommend this book for researchers in UAI. A word of caution: This is not a book for starters and those who do not have a well developed concept of uncertainty.
Rating:
Summary: A Pioneering Book on Causality
Review: This is a pioneering book dealing exhaustively with the subject of causation. Its contribution to the field of "Uncertainty in AI" is unmeasureable. It dealt with graphical models for reasoning in depth. For computer scientists looking for an encyclopedia of algorithms and applications on causation, there can not be a better book. I highly recommend this book for researchers in UAI. A word of caution: This is not a book for starters and those who do not have a well developed concept of uncertainty.
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