Book Review, 2017
By Alessio Farcomeni
Book Review, American Mathematical Society, MathSciNet, Mathematical Reviews, Review published March 29, 2017.
Despite the fact that quite a few high-quality books on the topic of causal inference have recently been published, this book clearly fills an important gap: that of providing a simple and clear primer. Its aim is explained in the preface: while causal questions motivate several data analysis problems, introductory textbooks seldom go beyond the "association does not imply causation" aphorism, possibly at most mentioning randomized experiments as the gold standard for assessment of causation. ... Read More

Book Review, 2017
By Keith A. Markus
Book Review,Structural Equation Modeling: A Multidisciplinary Journal, 24: 636-642, 2017.
Judea Pearl's work with various colleagues over the past several decades has had a tremendous influence on the theory and practice of causal inference. Written with Madelyn Glymour and Nicholas P. Jewell, Causal Inference in Statistics: A Primer offers a concise summary of and introduction to key elements of that body of work. ... Read More

May 2017
By Tomas J. Aragon, Amazon Customer on May 21, 2017
Format: Paperback Verified Purchase
I teach R programming to epidemiologists at UC Berkeley. I have appreciated the explosion of data science. However, epidemiologist are not statisticians or computer scientists. Epidemiologists have a central role is understanding causal inference for protecting and promoting health. Graphical causal models (causal graphs) provide a nexus that connects epidemiologists, statisticians, computer scientists to many other fields including machine learning, artificial intelligence, and even to plain old continuous quality improvement and population health improvement. In public health every intervention has a program theory: theory of causation, theory of change, and theory of action. These relationships can be represented with graphical causal models. Causal graphs can be used to capture expert knowledge from community stakeholders. We now have a common visual language to represent causal systems whether if comes from an academic endeavor, quality improvement on the frontlines of practice, or wisdom from community residents. Pearl's book is the first book that makes this world accessible to public health practitioners and epidemiologists like me. I am redesigning my whole course to around causal graphs for epidemiology and Bayesian decision networks for decision analysis. This book is a cause for celebration!

Causal inference -- so much more than statistics, October 2016
By Neil Pearce and Debbie A. Lawlor
Reviewed in International Journal of Epidemiology, 45(6):1895-1903.

It is perhaps not too great an exaggeration to say that Judea Pearl's work has had a profound effect on the theory and practice of epidemiology. Pearl's most striking contribution has been his marriage of the counterfactual and probabilistic approaches to causation.1 The resulting toolkit, particularly the use of counterfactual concepts and directed acyclic graphs (DAGs) has been extended by some epidemiologists to remarkable effect2,3 so that some problems which were previously almost intractable can now be solved relatively easily. What we previously tried to understand using words, probabilities and numerical examples can now be explored using causal diagrams, so that mindbending problems such as Berkson's Bias can be explained and understood relatively easily.4,5... (click for complete review)

The Next Big Thing in Quantitative Analysis, July 31, 2016
By Jim Grace, Lafayette, LA
Format: Verified Purchase
This review is from: Causal Inference in Statistics: A Primer (Kindle Edition) The book by Judea Pearl and collaborators Madelyn Glymour and Nicholas Jewell, Causal Inference in Statistics: A Primer, provides a concise introduction to a topic of fundamental importance for the enterprise of drawing scientific inferences from data. The book, which weighs in at a trim 125 pages, is written as a supplement to traditional training in statistics and I believe it fills that role admirably. I was very excited when my copy arrived because I am one of those folks who thinks that most statistics texts provide only the technical specs for quantitative science, not the driver's manual that is needed by researchers who collect and interpret data. After reading it, I think the book is going to be a big hit with both scientists and practicing statisticians. I believe will also prove to be useful support for those who teach statistics and data analysis ... Read More

March 2016
By Amazon Customer on March 24, 2016
Format: Paperback Verified Purchase
For a non-statistician interested in causal inference, this books gives an excellent introduction and grounding for tackling more scholarly works such as Peal's, "Introduction to Causal Inference" or his larger textbook. The writing is what really makes this book, the authors take their academician's hats off and just simply explain the topic with good use of examples that are easy to follow. With that said, the reader needs to be aware that the writing style does retain some old school academic hallmarks, such as the heavy use of semi-colons between realted independent clauses. This isn't a criticism, but rather an observation that goes to presentation style. At the moment I am a little more than halfway through the book and it is making thing topic accessiable to me, specifically by not leaving out small details, that to statisticians and mathematicians, may seem obvious or not worth mentioning. This ability to anticipate the students natural question is what makes this book so valuable!

I would recommend this book to anyone who has a at least a working knowledge of statistics. I would consider this book for an upper level undergrad course, and certainly one of the books for a graduate course on the topic. If Professor Pearl's lectures are anything like this book, I would enjoy sitting in on any lecture he gives.

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