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Causality: Models, Reasoning, and Inference
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. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable. Professor of Computer Science at the UCLA, Judea Pearl is the winner of the 2008 Benjamin Franklin Award in Computers and Cognitive Science.
400 pages, Hardcover
First published March 13, 2000
169 people are currently reading
3361 people want to read
About the author
Judea Pearl
39 books252 followersJudea 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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Displaying 1 - 22 of 22 reviews
February 6, 2017
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.
March 9, 2019
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 attempt to actually cover the whole field of causal inference; it's only about Pearl's work and that of his students.
What this book is really about is Pearl's mathematical "do-calculus", and how, given a complete causal graph, it can be used to rigorously state what it means to intervene or to assess a counterfactual.
For a brief introduction to using causal graphs to select your controls, see Chapter 17 of "Statistical Modeling - A Fresh Approach". That chapter is available free from the author at http://www.mosaic-web.org/go/Statisti...
For more about inferring causal graphs from the data, look for a series of papers by Colombo and Maathuis at ETH Zurich.
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 attempt to actually cover the whole field of causal inference; it's only about Pearl's work and that of his students.
What this book is really about is Pearl's mathematical "do-calculus", and how, given a complete causal graph, it can be used to rigorously state what it means to intervene or to assess a counterfactual.
For a brief introduction to using causal graphs to select your controls, see Chapter 17 of "Statistical Modeling - A Fresh Approach". That chapter is available free from the author at http://www.mosaic-web.org/go/Statisti...
For more about inferring causal graphs from the data, look for a series of papers by Colombo and Maathuis at ETH Zurich.
Currently reading
March 15, 2013A 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 SEM
3) Discuss about causality
4) Develop an algorithm using partial correlations to discover causal structure under certain assumptions
However, all the above has already made this book a must read for people in empirical research methods. The author made a lot of effort to convince the statistics community for the acceptance of his ideas. I think that is a wrong approach. His work is more useful to people using statistics for empirical research, than to statisticians.
Experts of research methods often say that “research methods do not equal to statistics”. Research methods equal statistics plus something else. Pearl’s work is to formalize this “something else” and provide tools to work on them explicitly. In other words, Pearl’s work can help us processing statistical results for causal analysis, but not much to improve statistical analysis.
In traditional empirical analysis, at least in the mainstream methods teaching, this “something else” for causal analysis is that variable A is a cause of variable B, if:
(1) A and B are correlated.
(2) The association arises because A causes B and not vice versa due to temporal or logical or theoretical reasons.
(3) The association between A and B is not spurious.
It seems to me that at least three parts of Pearl work are worth studying and even being applied to some empirical research projects.
(1) His work of explicitly defining the “something else”
(2) His work of formally representing them
(3) His work of developing rules and tools for us to handle them
After gaining a full understanding of the above three items, I think that we can use Pearl’s work to assist our causal analysis in empirical research.
According to Pearl, statistics deals with mean, variance, correlation, regression, dependence, conditional independence, association, likelihood, collapsibility, risk ratio, odd ratio, marginalization, conditionalization, “controlling for”, … While causal analysis deals with randomization, influence, effect, confounding, “holding constant”, disturbance, spurious correlation, instrumental variables, intervention, explanation, attribution, … The second part minus the first part is the “something else”.
Professor Pearl’s language to formally represent causal analysis and its components include both structural equation models (linear, nonlinear and nonparametric) and graphical diagrams. Pearl uses do(x) to represent intervention. As many methodologists will agree, with Pearl’s work, method concepts like spuriousness and confounding, are
much better formalized than ever before.
His proposed rules include criterion to select covariates for adjustment, intervention calculus, and counterfactual analysis. Professor Pearl also proposed IC* algorithm to discover causal structures.
These are good contributions made by Pearl’s work. But, this is just a beginning. In general, I think there are more questions than answers in this book. There are also many missing links we need to bridge, in order to conduct a good causal analysis. For example, indirect effects are not covered as much as the direct effects and total effects. How to
estimate the strength of a causal influence is also left out.
D.A. Freedman of UC Berkeley takes a different view than that of Pearl (Freedman 2004). Many scholars including Freedman mentioned that Pearl did not do any modeling or empirical work, but just talked causation mathematically or philosophically, that may not be a fair comment as theoretical discussion along can be very valuable. Due to this, Freedman claims that Pearl’s work is based on many assumptions that are unrealistic and difficulty to confirm in applied research. Freedman claims that Pearl acknowledged some of these assumptions like in page 83 of his book, but did not make all them clear.
Published in 1993 (2nd edition in 2000 by MIT Press), the book Causation, Prediction and Search by Spirtes, Glymour, and Scheines (SGS) is worth reading as they actually developed a software for their developed algorithms and applied to a lot of real research. Between SGS and Freedman, there are also many dialogues in discussing whether the work from statistical evidence to causal inference can be automated without any needs for subject knowledge.
Actually, both the algorithms developed by Pearl and SGS do not work well. Professor Freedman of UC Berkeley claims these algorithms do not work as they are based on false assumptions. As I know, quite many scholars including myself tried these algorithms on some empirical data, and found these algorithms often lead us to nowhere or to some
errors. However, many ideas presented in these algorithms can be used, in combination with subject knowledge and other statistical methods like structural equation modeling method, to aid us in generating hypotheses and also in testing fitted models. Professor Bill Shipley has some good work along this line (Shipley 2000).
In general, I believe to successfully infer causality from statistical evidence like correlation does require some subject knowledge, additional statistical methods and hard work. But, the work of Pearl and SGS can help to improve the current practice greatly.
Reference
Freedman D. A. 2004 Statistical Models for Causation, UCB Statistics Technical Report No. 651 www.stat.berkeley.edu/-census/651.pdf
Freedman D. A. 1998 Are There Algorithms That Discover Causal Structure, UCB Statistics Technical Report No. 514
Halpern, Joseph Y. and Judea Pearl 2001 Cause and Explanations: A Structural-Model Approach, in Proceedings of the Seventeenth Conference on Uncertainy inartificial Intelligence, San Francisco, CA: Morgan Kaufmann
Heckerman, David and Ross Shachter 1995 Decision-theoretic foundation for causal reasoning, Journal of Artificial Intelligence Research 3: 405-430
Pearl, Judea 2003 Statistics and Causal Inference: A Review, Test (2003) Vol. 12, No. 2:281-345
Newberg, Leland Gerson 2003 Review of Causality Econometric Theory, 19, 2003: 675-685
Shipley, Bill 2000 Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference. Cambridge University Press
Spirtes, P., Glymour, C., and Scheines, R. 1993 Causation, Prediction, and Search. Springer Lecture Notes in Statistics, no. 81, Springer-Verlag, New York. 2nd edn. 2000, MIT Press
***
This note was written when Alex worked in IBM Research from Dec 2004 to April 2005, then was modified in August 2005. The author benefited from discussion on this matter with Dr. Sunil Noronha and Joseph Kramer of IBM Research.
For further work of Dr. Alex Liu on this subject, please visit below for his book ~ From Model Building to Model Mapping:
http://www.researchmethods.org/modeli...
Or visit below for the RM software where causality reasoning and techniques have been incorporated.
http://www.researchmethods.org/rmplat...
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 SEM
3) Discuss about causality
4) Develop an algorithm using partial correlations to discover causal structure under certain assumptions
However, all the above has already made this book a must read for people in empirical research methods. The author made a lot of effort to convince the statistics community for the acceptance of his ideas. I think that is a wrong approach. His work is more useful to people using statistics for empirical research, than to statisticians.
Experts of research methods often say that “research methods do not equal to statistics”. Research methods equal statistics plus something else. Pearl’s work is to formalize this “something else” and provide tools to work on them explicitly. In other words, Pearl’s work can help us processing statistical results for causal analysis, but not much to improve statistical analysis.
In traditional empirical analysis, at least in the mainstream methods teaching, this “something else” for causal analysis is that variable A is a cause of variable B, if:
(1) A and B are correlated.
(2) The association arises because A causes B and not vice versa due to temporal or logical or theoretical reasons.
(3) The association between A and B is not spurious.
It seems to me that at least three parts of Pearl work are worth studying and even being applied to some empirical research projects.
(1) His work of explicitly defining the “something else”
(2) His work of formally representing them
(3) His work of developing rules and tools for us to handle them
After gaining a full understanding of the above three items, I think that we can use Pearl’s work to assist our causal analysis in empirical research.
According to Pearl, statistics deals with mean, variance, correlation, regression, dependence, conditional independence, association, likelihood, collapsibility, risk ratio, odd ratio, marginalization, conditionalization, “controlling for”, … While causal analysis deals with randomization, influence, effect, confounding, “holding constant”, disturbance, spurious correlation, instrumental variables, intervention, explanation, attribution, … The second part minus the first part is the “something else”.
Professor Pearl’s language to formally represent causal analysis and its components include both structural equation models (linear, nonlinear and nonparametric) and graphical diagrams. Pearl uses do(x) to represent intervention. As many methodologists will agree, with Pearl’s work, method concepts like spuriousness and confounding, are
much better formalized than ever before.
His proposed rules include criterion to select covariates for adjustment, intervention calculus, and counterfactual analysis. Professor Pearl also proposed IC* algorithm to discover causal structures.
These are good contributions made by Pearl’s work. But, this is just a beginning. In general, I think there are more questions than answers in this book. There are also many missing links we need to bridge, in order to conduct a good causal analysis. For example, indirect effects are not covered as much as the direct effects and total effects. How to
estimate the strength of a causal influence is also left out.
D.A. Freedman of UC Berkeley takes a different view than that of Pearl (Freedman 2004). Many scholars including Freedman mentioned that Pearl did not do any modeling or empirical work, but just talked causation mathematically or philosophically, that may not be a fair comment as theoretical discussion along can be very valuable. Due to this, Freedman claims that Pearl’s work is based on many assumptions that are unrealistic and difficulty to confirm in applied research. Freedman claims that Pearl acknowledged some of these assumptions like in page 83 of his book, but did not make all them clear.
Published in 1993 (2nd edition in 2000 by MIT Press), the book Causation, Prediction and Search by Spirtes, Glymour, and Scheines (SGS) is worth reading as they actually developed a software for their developed algorithms and applied to a lot of real research. Between SGS and Freedman, there are also many dialogues in discussing whether the work from statistical evidence to causal inference can be automated without any needs for subject knowledge.
Actually, both the algorithms developed by Pearl and SGS do not work well. Professor Freedman of UC Berkeley claims these algorithms do not work as they are based on false assumptions. As I know, quite many scholars including myself tried these algorithms on some empirical data, and found these algorithms often lead us to nowhere or to some
errors. However, many ideas presented in these algorithms can be used, in combination with subject knowledge and other statistical methods like structural equation modeling method, to aid us in generating hypotheses and also in testing fitted models. Professor Bill Shipley has some good work along this line (Shipley 2000).
In general, I believe to successfully infer causality from statistical evidence like correlation does require some subject knowledge, additional statistical methods and hard work. But, the work of Pearl and SGS can help to improve the current practice greatly.
Reference
Freedman D. A. 2004 Statistical Models for Causation, UCB Statistics Technical Report No. 651 www.stat.berkeley.edu/-census/651.pdf
Freedman D. A. 1998 Are There Algorithms That Discover Causal Structure, UCB Statistics Technical Report No. 514
Halpern, Joseph Y. and Judea Pearl 2001 Cause and Explanations: A Structural-Model Approach, in Proceedings of the Seventeenth Conference on Uncertainy inartificial Intelligence, San Francisco, CA: Morgan Kaufmann
Heckerman, David and Ross Shachter 1995 Decision-theoretic foundation for causal reasoning, Journal of Artificial Intelligence Research 3: 405-430
Pearl, Judea 2003 Statistics and Causal Inference: A Review, Test (2003) Vol. 12, No. 2:281-345
Newberg, Leland Gerson 2003 Review of Causality Econometric Theory, 19, 2003: 675-685
Shipley, Bill 2000 Cause and Correlation in Biology: A User’s Guide to Path Analysis, Structural Equations and Causal Inference. Cambridge University Press
Spirtes, P., Glymour, C., and Scheines, R. 1993 Causation, Prediction, and Search. Springer Lecture Notes in Statistics, no. 81, Springer-Verlag, New York. 2nd edn. 2000, MIT Press
***
This note was written when Alex worked in IBM Research from Dec 2004 to April 2005, then was modified in August 2005. The author benefited from discussion on this matter with Dr. Sunil Noronha and Joseph Kramer of IBM Research.
For further work of Dr. Alex Liu on this subject, please visit below for his book ~ From Model Building to Model Mapping:
http://www.researchmethods.org/modeli...
Or visit below for the RM software where causality reasoning and techniques have been incorporated.
http://www.researchmethods.org/rmplat...
May 10, 2021
Scientists regarded causality with suspicion. Statistics was developed around correlation and without causality. Philosophers classified causality as a metaphysical concept. Pearl believes that causality belongs to science – in particular to Statistics and AI; and accordingly developed his new science of causality. Beyond the rigorous formalism and axiomatic approach in this book, causality is incorporated into this new science through diagrams. “The Book of Why” (https://www.goodreads.com/book/show/3...) is the popular and more accessible version of this one.
Currently reading
February 7, 2010You really can infer causation from correlation (with a few caveats).
September 9, 2019
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.
April 20, 2023
"Causality: Models, Reasoning, and Inference" by Judea Pearl is a thought-provoking and significant contribution to the fields of statistics, artificial intelligence, and philosophy. Pearl offers innovative and groundbreaking ideas on causal modeling and reasoning, while also providing practical guidance for conducting research in these areas.
The book is well-written and comprehensively covers various aspects of causality, from graphical models to counterfactuals. It offers a wealth of information and tools that can be valuable to researchers and practitioners alike. Pearl's work has undoubtedly advanced our understanding of causal relationships, and his ideas have been widely adopted in several disciplines.
However, I couldn't help but notice an ironic inconsistency in Pearl's work. While the book provides a thorough exploration of causality, it is somewhat surprising to learn that the author himself does not believe in a first cause of creation. This omission is particularly striking given the central role causality plays in understanding the origins of phenomena.
As a result, the book feels incomplete in certain respects. Readers who are interested in exploring the philosophical implications of causality, particularly with regards to the existence of a first cause, may be left unsatisfied by Pearl's perspective. This incongruity detracts from the overall impact of the book and may limit its appeal to a broader audience.
Despite this shortcoming, "Causality: Models, Reasoning, and Inference" remains an important and influential work in its field. While it may not provide a comprehensive exploration of all aspects of causality, it is still a valuable resource for those seeking to advance their understanding of causal modeling and inference.
The book is well-written and comprehensively covers various aspects of causality, from graphical models to counterfactuals. It offers a wealth of information and tools that can be valuable to researchers and practitioners alike. Pearl's work has undoubtedly advanced our understanding of causal relationships, and his ideas have been widely adopted in several disciplines.
However, I couldn't help but notice an ironic inconsistency in Pearl's work. While the book provides a thorough exploration of causality, it is somewhat surprising to learn that the author himself does not believe in a first cause of creation. This omission is particularly striking given the central role causality plays in understanding the origins of phenomena.
As a result, the book feels incomplete in certain respects. Readers who are interested in exploring the philosophical implications of causality, particularly with regards to the existence of a first cause, may be left unsatisfied by Pearl's perspective. This incongruity detracts from the overall impact of the book and may limit its appeal to a broader audience.
Despite this shortcoming, "Causality: Models, Reasoning, and Inference" remains an important and influential work in its field. While it may not provide a comprehensive exploration of all aspects of causality, it is still a valuable resource for those seeking to advance their understanding of causal modeling and inference.
April 9, 2022
It took me roughly three months to finish this. I "discovered" this while investigating causal interpretations of models that I had been working with. I had to make notes after a couple of chapters.
We all appreciate ideas that take us by storm. And this was a cornucopia of those. At times it felt like I am reading either sci-fi or pseudoscience. I definitely did not have the capacity to refer to all the papers or verify all proofs myself. They gave the guy a Turing prize for this theory so I'd just trust the peer review.
Lastly and leastly, as a negative, the author is a terrible writer. The book was sometimes difficult to read simply because of the bad writing.
We all appreciate ideas that take us by storm. And this was a cornucopia of those. At times it felt like I am reading either sci-fi or pseudoscience. I definitely did not have the capacity to refer to all the papers or verify all proofs myself. They gave the guy a Turing prize for this theory so I'd just trust the peer review.
Lastly and leastly, as a negative, the author is a terrible writer. The book was sometimes difficult to read simply because of the bad writing.
July 15, 2019
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, clarifying, explaining things. On the other hand, this book would be better if it includes more in: real life applications in multiple disciplines, practical issues when applying the logic/approaches introduced by author. I think there is still a lot more to explore in causal inference area.
In summary, this book talks: a theoretical/mathematical framework of causality world. It creates a language system defining, clarifying, explaining things. On the other hand, this book would be better if it includes more in: real life applications in multiple disciplines, practical issues when applying the logic/approaches introduced by author. I think there is still a lot more to explore in causal inference area.
October 25, 2021
I see this as a philosophical work. The quote that I liked most: "The utility of understanding how television works comes not from turning the knobs correctly but from the ability to repair a TV set when it breaks down."
read-in-part
July 2, 2015fucking "back door" y "front door" =)
February 21, 2017
very hard to get all of the way through. I think I actually only got 3/4 of the way through in the end...
August 1, 2017
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.
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.
March 5, 2021
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.
April 3, 2023
This book is an undertaking, and to ease my load, I began with the appendix, then the examples, then the proofs. I look forward to using Pearl's calculus of causality in my econometrics work and to improve my rational thinking in general.
January 13, 2018
In the future people will regard this as on the same level as Newton’s “Principia” or Frege’s “Begriffsscrift.”
December 26, 2018
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.
December 31, 2018
Doesn't answer the question in the title.
June 7, 2019
Book surveyed marked.
April 7, 2020
A fantastical epic through circular reason and academic self-delusion. How Pearl has any following is likely due to wishful thinking
November 12, 2020
Dense at times, could do with some more formulas instead of diagrams. 😉
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February 18, 2015Location: GG6 IRC
Accession no: DL026784
Accession no: DL026784
Displaying 1 - 22 of 22 reviews