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Causality: Models, Reasoning, and Inference

4.15  ·  Rating details ·  259 ratings  ·  17 reviews
Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. P ...more
Hardcover, 400 pages
Published March 13th 2000 by Cambridge University Press
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Michael Nielsen
Jan 06, 2015 rated it it was amazing
Historically, it's a strange fact that we developed probability and statistics without also developing a theory of causality. Such a theory would dramatically change science. This book summarizes recent attempts by Pearl and others to develop such a theory. I don't think the theory is complete, but this is a great prelude.
...more
John Ledesma
A Note On “Causality: Models, Reasoning, and Inference” by Judea Pearl
By Dr. Alex Liu

August 2005 ***

This is a note on my reading Judea Pearl’s book “Causality: Models, Reasoning, and Inference” 1999 Cambridge University Press.

Even it sounds like the book is creating a NEW paradigm of conducting causal research,to many empirical scholars including me; the main purpose of this book is to:

1) Develop graphical tools in assisting causal analysis
2) Develop a non-linear and non-parametric extension of
...more
Terran M
Jun 03, 2018 rated it it was ok  ·  review of another edition
This should not be your first book on causality. Start with Kline, and if you finish that book and want more on SCM, then come back to this book. Another reasonable place to start would be Mostly Harmless Econometrics.

The problem is that Pearl, who is undeniably a significant contributor to the field, is not a good writer. He does not explain concepts clearly, and he cares more about promoting his own contributions than educating. Although this book has a general-sounding title, it makes no atte
...more
Moshe
Feb 07, 2010 is currently reading it
You really can infer causation from correlation (with a few caveats).
Tinwerume
Feb 20, 2019 rated it it was ok
Shelves: mathematics
It's very much not written like a math book. Definitions are fairly loose, and theorems are rarely marked as such so it's difficult to distinguish mathematical claims from philosophical ones. ...more
Bing Wang
Jul 15, 2019 rated it really liked it  ·  review of another edition
A good book worth reading for anyone with math/stat background and also would love to learn causal inference. Core chapters include chap 2,3,4,7. However, the biggest pain point for me reading this book is thinking where and how I could apply the idea and approaches that I learned into my daily data science work. It's still a little bit hard for me to think of a clear way.

In summary, this book talks: a theoretical/mathematical framework of causality world. It creates a language system defining,
...more
Leonardo
May 12, 2014 marked it as read-in-part
fucking "back door" y "front door" =) ...more
Makoto
Feb 21, 2017 rated it liked it
very hard to get all of the way through. I think I actually only got 3/4 of the way through in the end...
Aleksandar
Mar 05, 2021 rated it liked it
I slogged through this book and it was not worth it. If you want to learn about causality, find another book. This one is more like a manual full of proved theorems and definitions. No exercises, few examples, and little intuition. Definitely not for laymen, and researchers are better off reading some shorter primers.
Ari
Aug 01, 2017 rated it liked it
Shelves: abandoned
The first few chapters are full of ideas, and I found the graphical model of causality a powerful conceptual tool. This is the premiere exposition of that view.

The wife, who is a statistics graduate student, is more skeptical and thinks that other models are as good or better.

I read about half of it; the rest was too technical for my state of mind and needs.
Zak Boston
Apr 07, 2020 rated it it was ok
A fantastical epic through circular reason and academic self-delusion. How Pearl has any following is likely due to wishful thinking
ShawnLeeZX
Jun 06, 2019 rated it really liked it
Shelves: machine-learning
Book surveyed marked.
Thomas Eapen
Dec 26, 2018 rated it it was amazing
The classic modern reference on the science and philosophy of causality. However, it can be a challenging read for those who are not familiar with probabilistic models.
Chris
Dec 31, 2018 rated it it was ok
Shelves: statistics
Doesn't answer the question in the title. ...more
David Sundahl
Jan 13, 2018 rated it it was amazing
In the future people will regard this as on the same level as Newton’s “Principia” or Frege’s “Begriffsscrift.”
Pieter
Nov 11, 2020 rated it really liked it
Dense at times, could do with some more formulas instead of diagrams. 😉
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Accession no: DL026784
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Judea Pearl is an Israeli-American computer scientist and philosopher, best known for championing the probabilistic approach to artificial intelligence and the development of Bayesian networks.

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“The two fundamental questions of causality are: (1) What empirical evidence is required for legitimate inference of cause–effect relationships? (2) Given that we are willing to accept causal information about a phenomenon, what inferences can we draw from such information, and how?” 0 likes
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