**REVIEWS OF
CAUSAL INFERENCE IN STATISTICS: A PRIMER**

**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 effect^{2,3} 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.