Rating: 
Summary: What is the cause of intolerance?
Review:
Pearl included an Epilogue containing a lecture he gave in 1996 entitled, "The Art and Science of Cause and Effect."
Pearl concludes the lecture 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."
Unfortunately, virtually no advances have been made in learning the causation of intolerance nor how to rid us of it. And Judea Pearl suffered immensely because of that . . . Daniel Pearl, his son, was killed in Iraq due to intolerance. :-(
BTW, the book is great.
Rating: 
Summary: Causality can be understood in simple mathematical terms
Review: ...................................................................................I got my first hint of the dark world of causality during my junior year of high school. My science teacher, Dr. Feuchtwanger, introduced us to the study of logic by discussing the 19th century finding that more people died from smallpox innoculations than from smallpox itself. Some people used this information to argue that innoculation was harmful when, in fact, the data proved the opposite, that innoculation was saving lives by irradicating smallpox. ``And here is where logic comes in," concluded Dr. Feuchtwanger, "To protect us from cause-effect fallacies of this sort." We were all enchanted by the marvels of logic, even though Dr. Feuchtwanger never actually showed us how logic protects us from such fallacies. It doesn't, I realized years later as an artificial intelligence researcher. Neither logic, nor any branch of mathematics had developed effective tools for managing problems, such as the smallpox innoculations, involving cause-effect relationships. Most of my colleagues even considered causal vocabulary to be dangerous, avoidable, ill-defined, and nonscientific. ``Causality is endless controversy,'' one of them warned. The accepted style in scientific papers was to write ``A implies B'' even if one really meant ``A causes B,'' or to state ''A is associated with B" if one was thinking ''A affects B''. Clearly, such denial of causal intuition could not last forever. The influence of artificial intelligence and the availability of powerful computer languages gave my generation the expectation that intuition should be expressed, not suppressed. And causality, it turns out, is not nearly as nasty as her reputation suggests. Once I got past a few mental blocks, I found causality to be smiling with clarity, bursting with new ideas and new possibilities. As the epilogue of my book summarizes: "Causality is not mystical or metaphysical. It can be understood in terms of simple processes, and it can be expressed in a friendly mathematical language, ready for computer analysis." My intended audience includes: students of statistics who wonder why instructors are reluctant to discuss causality in class; students of epidemiology who wonder why simple concepts such as "confounding" are so terribly complex when expressed mathematically; students of economics and social science who often doubt the meaning of the parameters they estimate; and, naturally, students of artificial intelligence and cognitive science, who write programs and theories for knowledge discovery, causal explanations and causal speech. I have aimed to provide each of these groups with separate ideas and techniques to make causal inference easier in their respective fields. The techniques will be a success only if they help resolve challenging problems in those fields, and I am fairly confident they will.
Rating: 
Summary: The best and only on the topic
Review: A great text, if for no other reason than the fact that it fills an important niche. Pearl does an excellent job of delineating causal models as both philosophical and statistical problems. I found the coverage of latent variable models particularly useful. My only complaint is Pearl often makes assumptions without justifying them sufficiently. Usually, the assumptions made are reasonable or of negligible consequence, but at other times, the veracity of the assumptions is arguably core matter of the discussion. The net effect is a feeling of reading a brilliant, detailed exposition of what causal models imply observationally, undermined by doubts about the appropriateness of causality as a concept at all. Overall, however, this a wonderful text that should be useful to anyone interested in causality or statistical modeling.
Rating:
Summary: A "Radically New perspective on Causation"
Review: Choice (November 00) calls both Pearl's Causality (and Juarrero's Dynamics in Action, which Choice reviews together with Pearl), a "radically new perspective on causation and human behavior... Pearl critically reviews the major literature on causation, both in philosohy and in applied statistics in the social sciences. His formal models, nicely illustrated by practical examples, show the power of positing objectdively real causation connetions, counter to Hume's skepticism, which has dominated discussions of causality in both analytic philosophy and statistical analysis. Probabilities, Pearl argues, reflect subjective degrees of belief, whereas causal relations describe objective physical constraints. He reveals the role of substantive causes in statistical analyses in examples from medicine, economics, and policy decisions. "Both works are highly ambitious in rejecting traditional views. Although the arguments ar meticulous and represent intensive research, their criticisms of mainstream traditions are destined to arouse controversy... Juarrero and Pearl's books will greatly interest philosophers and scientists who are concerned with causality and the explanation of human behavior."
Rating:
Summary: A "Radically New perspective on Causation"
Review: Choice (November 00) calls both Pearl's Causality (and Juarrero's Dynamics in Action, which Choice reviews together with Pearl), a "radically new perspective on causation and human behavior... Pearl critically reviews the major literature on causation, both in philosohy and in applied statistics in the social sciences. His formal models, nicely illustrated by practical examples, show the power of positing objectdively real causation connetions, counter to Hume's skepticism, which has dominated discussions of causality in both analytic philosophy and statistical analysis. Probabilities, Pearl argues, reflect subjective degrees of belief, whereas causal relations describe objective physical constraints. He reveals the role of substantive causes in statistical analyses in examples from medicine, economics, and policy decisions. "Both works are highly ambitious in rejecting traditional views. Although the arguments ar meticulous and represent intensive research, their criticisms of mainstream traditions are destined to arouse controversy... Juarrero and Pearl's books will greatly interest philosophers and scientists who are concerned with causality and the explanation of human behavior."
Rating: 
Summary: A review of "Causality"
Review: First off, the rating of three stars is relative to my expectations that this book would provide me with some insights in how to use graphical models for purposes of making inferences from statistical data and, in general, to facilitate the process of (machine) learning from data. And although Pearl and his colleagues have made great progress in this area, this book seems more targeted for researchers in areas outside of AI, such as economics, statistics, and medical research. Although the author gives a number of rigorous definitions to help support his notions of causality, the book is written in a somewhat abstract manner with few if any nontrivial examples (although enough trivial ones to satisfy a more general audience) to support the definitions and concepts. References to the literature are favored over mathematical proofs. Thus, aside from the references, I found this book of little use, but on the other hand, I do recommend it for its intended audience, for I do believe that graphical models can be of great use in these other areas. Finally given the controversy and general misunderstanding about "causality", I wonder why Pearl would even use definitions like "causal model" and "...variable X is a causal influence of variable Y". His justification seems that researchers still think in terms of cause and effect, and thus it would serve them well if they had a mathematical foundation to fall back on.
Even if I did not have issue with some of the techniques and algorithms endorsed in this book, it would still seem much more appropriate to supply fresh, distinguished definitions (devoid of the "cause" word and its synonyms) and thus when future researchers use and make reference to Pearl's structural methods, they will call them as such and hopefully avoid confusion and controversy.
Rating:
Summary: A "Radically New perspective on Causation"
Review: I take issue with the previous reviewer. Pearl does not assume that the modeller is able, a priori, to determine what the correct model is. Instead, Pearl asks what conclusions can be drawn if the modeller is able to substantiate only parts of the model. By systematically changing those parts, he then obtains a full picture of what modeling assumptions "must" be substantiated before causal inferences can be derived from nonexperimental data. An anslysis of assumptions is not a license to abuse them.
Rating:
Summary: Understanding causality poses no danger!
Review: I take issue with the previous reviewer. Pearl does not assume that the modeller is able, a priori, to determine what the correct model is. Instead, Pearl asks what conclusions can be drawn if the modeller is able to substantiate only parts of the model. By systematically changing those parts, he then obtains a full picture of what modeling assumptions "must" be substantiated before causal inferences can be derived from nonexperimental data. An anslysis of assumptions is not a license to abuse them.
Rating:
Summary: Pearl summarizes his work on causation.
Review: Judea Pearl and his colleagues at UCLA (and elsewhere) have published a large number of papers and written unpublished reports over the past 15 years, in which they have developed a modern, analytical approach to causation. Many of these are in somewhat obscure publications, and so it is especially helpful to have the most important of them collected together in this volume. Pearl has edited, written new chapters and connecting prose, to weave this summary of a substantial amount of research. Although the dust-jacket suggests that only modest mathematics is needed, and although this is technically true, it is misleading, because the whole area requires a sophistication of thought that goes well beyond the simplicity of the tools. Nonetheless, there is currently no other volume that is as easy to read as this, and summarizes so much material so compactly. It is possible that the new vision of causal analysis developed by Spirtes, Scheines, Glymour, Pearl, Robins, Verma, Heckerman, Meek, and others, will have profound effect on how we analyze research data. If so, this book will be necessary reading for decades to come.
Rating: 
Summary: Wishful Reasoning
Review: Pearl supposes that the modeller is able, a priori, to determine, *exactly* what the correct model is. One must be able to specify the model correctly, knowing what the possible confounding variables are, what moderators are important, etc., in advance. How reasonable is this? Isolation and pseudo-isolation are 'swept under the rug,' with an inadequate interpretation of the error term. This is dangerous work that will lead to situations where a researcher will calculate a path model using nonexperimental, cross-sectional data and conclude that they have found a 'causal' model with their X's having a cause-and-effect relationship with their endogenous variables. It is easy to imagine a saturated path model with cross-sectional survey data that produces 'structural' coefficients that are large relative to their standard errors by virtue of a large sample size, yet 'causes effects' that are relatively small. Without proper consideration of the effect sizes or alternative models, the researcher will cite Pearl for the value of their work (no puns about pearls and swine:). I doubt that Pearl has thoroughly thought through the garbage that will be printed citing him as justification; at least, I hope he hasn't.
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