Causality: Models, Reasoning, and Inference

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.


Pearl's view on causality   February 22, 2008
Michael R. Chernick (Holland PA)
25 out of 25 found this review helpful

Judea Pearl is one of the leading researchers in the topic of casuality. What is causality? In the exploration of statistical data we are often able to find relationships or correlations between two variables. We are often tempted to attribute the results of one variable, say A as an outcome (being high or low)that is due to the result (high or low) of the other, say B. We want to say that B is the cause of the outcome of A. Significant correlation by itself only suggests relationships. It cannot tell you whether A causes B or B causes A or neither. Causility is the study of designing experiments to allow you to determine if a relationship has a cause and effect. The subject matter is very philosophical and somewhat controversial. But a lot of research effort has gone into providing mathematical rigor to the concept. Pearl is one of those rare scientists who can contribute to such theory and explain it. But as Aickin suggests in his amazon review this is not a subject for a novice. Previous exposure to statistical methods such as correlation and regression is important to a clear unbderstanding of this book.


The best and only on the topic   May 20, 2001
22 out of 22 found this review helpful

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.


A "Radically New perspective on Causation"   June 9, 2001
26 out of 30 found this review helpful

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."


Important but difficult   September 15, 2004
Peter McCluskey (Mountain View, CA USA)
27 out of 32 found this review helpful

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.