7 ms·

The Book of Why: The New Science of Cause and Effect [pdf]

193 points by elcritch 6y ago

pavlov 6y ago
It's a great book; an enjoyable read even for someone like me who's mostly afraid of statistics. Pearl's enthusiasm for the topic is palpable, and he seems to have the academic pedigree to match the occasionally lofty claims. The book makes a very clear case in walking the layperson through various causal techniques and underlining how they differ from traditional methods. Few popular science books actually go into such detail.
- tgb 6y ago
  It's a good book, but some may be turned off by the first chapter or so that reads like he's trying to rub his victories into the faces of his academic adversaries. It had more than a little air of gloating to it, in my opinion, and the book would have been better without that. (Though he spent considerable time on the earlier precedents for his work, which I had previously thought was entirely novel.)
pjmorris 6y ago
For balance and discussion, Andrew Gelman's critical review of 'The Book of Why': https://statmodeling.stat.columbia.edu/2019/01/08/book-pearl-mackenzie/ https://statmodeling.stat.columbia.edu/2019/01/08/book-pearl...
- epistasis 6y ago
  Thanks for sharing this! I knew that Gelman and Pearl have been going back and forth for years, and it's great to see this. I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. That said, I think their disagreements don't have much effect on practice, and have as much to do with their different academic lineages as anything else. I expect that any dispute will be resolvable as the science progresses
  - elcritch 6y ago
    > I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. Are you able to elaborate?! It'd be really interesting to hear what/if tooling exists for these graphs. I just found the book recently and am listening to the Audible version. It also reaffirms my desire for a "Bayesian inference" spreadsheet like tool. Something that'd help organize a few dozen thoughts ideas for researchers/engineers.
    - currymj 6y ago
      R package "daggity" is excellent. http://www.dagitty.net/ http://www.dagitty.net/
    - epistasis 6y ago
      I've used libDAI in the past, but I'm not sure if it's maintained anymore. I've come across several other toolkits that looked interesting since then, but never had the right project. There's a group at (CMU?)/Pitt that I think now has a Python package for causal inference in particular. (Causality is far more complicated and harder to discover than a lot of the stuff I work towards though, which is all in biology.)
    - canjobear 6y ago
      > a "Bayesian inference" spreadsheet like tool http://probcomp.csail.mit.edu/software/bayesdb/ http://probcomp.csail.mit.edu/software/bayesdb/
    - sjg007 6y ago
      Thousands and millions of variables approaches deep learning w/ softmax. It is difficult to see how it would work any other way. I mean would you want to have a causal model of particles?
  - lalaland1125 6y ago
    Why would Pearl's approach be better at scaling? If anything, the Rubin causal model scales wonderfully with more variables.
- wavegeek 6y ago
  This is a strange debate. They seem to be in violent agreement.
  - anigbrowl 6y ago
    https://en.wikipedia.org/wiki/Narcissism_of_small_differences https://en.wikipedia.org/wiki/Narcissism_of_small_difference...
- stewbrew 6y ago
  Balance is the wrong word. you might want to check first who was AG's doctor father. Surprise, it was Rubin.
wavegeek 6y ago
Great book that clearly explains the perils or inferring causation and now you can deal with them constructively. For those who want more he has a more technical book "Causality: Models, Reasoning and Inference" which is also excellent. Pearl is a legend in the field, who wrote the seminal "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference". There is a school of thought that there is nothing new here. Pearl is very honest and open about who invented what and of the mistakes in his earlier work. While no one finding is entirely new here, the overall package adds up to a lot IMHO. And this after having intensively studied statistics and probability over many years. It really changed my approach to and understanding of causality. And most importantly it gave me reliable intuitions on the subject. After his books, things seem obvious to me that others struggle with.
- zwaps 6y ago
  I like Pearl's work a lot more than his opinions on who invented what, and who is right about what. For that reason, I would wholeheartedly recommend his books but I would also recommend skipping any sideshows about related literature. Fact is, Pearl selectively picks literature concerned with causality and, in particular, literature not successfully tackling the subject. He does ignore many other approaches to the issue, especially parallel developments to tackle problems in fields that he specifically critiques. In other words: Pearl, next to being a great researcher, is also a showman who knows how to build a following. The essence of the debate is this: Neither Pearl's framework, nor anyone else's capture all valid approaches to causal inference. One can construct cases in Rubin's framework that DAG can not solve and vice-versa. The downside to Pearl's approach is that it is - right now - more difficult to implement. The cases where DAG undoubtedly succeeds better than other approaches are, in a sense, unlikely to succeed as a practical research projects. That being said, a great strength of such graphical models is that they allow quite sophisticated reasoning in several well-known simple but non-intuitive cases. Such reasoning otherwise requires an immense amount of experience and / or education on the pitfalls of causal inference. That is also a reason why I would like to see this framework taught more in schools. All in all, as another great post in this thread has pointed out, much of the debate is in violent agreement on base issues. Once this issue transcends the egos involved, much progress will be made and that is, in my view, very exciting.
Anon84 6y ago
If you’re interested in learning more about causality, you might be interested in my blog series working through Pearls “Causal Inference in Statistics: A Primer”: https://github.com/DataForScience/Causality https://github.com/DataForScience/Causality (Jupiter notebooks and links to blog posts)
yashevde 6y ago
Thanks for sharing, this makes me want to move the book up my list of to-reads. As a side-note, I’m blown away to learn that Judea Pearl is the father of Daniel Pearl — can’t imagine the amount of composure it must take to continue to operate at the highest levels in his field after experiencing grief of that nature.
bachmeier 6y ago
Recognizing that not many will care about my opinion on the subject, I will link to this excellent (and IMO accurate) paper by Guido Imbens: https://arxiv.org/pdf/1907.07271.pdf https://arxiv.org/pdf/1907.07271.pdf One of the problems in groups is that there is a belief that the loudest and most self-confident individual is correct. I believe that is the case with Pearl. He attracts a following based on his personality, but when it comes to actually doing empirical research, he comes up short. "Separate from the theoretical merits of the two approaches, another reason for the lack of adoption in economics is that the DAG literature has not shown much evidence of the alleged benefits for empirical practice in settings that resonate with economists....In contrast in the DAG literature, TBOW, [Pearl, 2000], and[Peters, Janzing, and Sch ̈olkopf, 2017] have no substantive empirical examples, focusing largely on identification questions in what TBOW refers to as “toy” models. Compare the lack of impact of the DAG literature in economics with the recent embrace of regression discontinuity designs imported from the psychology literature, or with the current rapid spread of the machine learning methods from computer science, or the recent quick adoption of synthetic control methods developed in economics [Abadie and Gardeazabal, 2003, Abadie, Diamond, and Hainmueller,2010]. All three came with multiple concrete and detailed examples that highlighted their benefits over traditional methods. In the absence of such concrete examples the toy models in the DAG literature sometimes appear to be a set of solutions in search of problems, rather than a set of clever solutions for substantive problems previously posed in social sciences, bringing to mind the discussion of Leamer on the Tobit model ([Leamer, 1997])."
- longtimegoogler 6y ago
  That's a long paper and I am interested to read it. From what I have heard, there is more adoption of Pearl's methods in Epidemiology and in Social Sciences, in contrast with Econometrics. To me, it reminds me of Dyson Freeman's, "Birds and Frogs". For most Statistician's and Economists Regression is their goto tool and the Potential Outcome Framework is much more natural to them. You could say the same about the synthetic controls methods you mentioned.
- squibbles 6y ago
  I find this dialog to be reminiscent of the controversy surrounding Wolfram's A New Kind of Science. Perhaps one could frame it as the theorists versus the pragmatists.
rahimnathwani 6y ago
If you're interested in learning more about causal inference and 'do calculus', you might like this course and free textbook: https://www.bradyneal.com/causal-inference-course https://www.bradyneal.com/causal-inference-course
transfire 6y ago
"Why" is not a query of causality, but rather purpose. The proper word would be "How".
- kgwgk 6y ago
  https://en.wikipedia.org/wiki/Four_causes https://en.wikipedia.org/wiki/Four_causes Aristotle holds that there are four kinds of answers to "why" questions: Matter (the material cause of a change or movement): The aspect of the change or movement that is determined by the material that composes the moving or changing things. For a table, such might be wood; for a statue, such might be bronze or marble. Form (the formal cause of a change or movement): A change or movement caused by the arrangement, shape, or appearance of the thing changing or moving. Aristotle says, for example, that the ratio 2:1, and number in general, is the cause of the octave. Agent (the efficient or moving cause of a change or movement): Consists of things apart from the thing being changed or moved, which interact so as to be an agency of the change or movement. For example, the efficient cause of a table is a carpenter, or a person working as one, and according to Aristotle the efficient cause of a boy is a father. End or purpose (the final cause of a change or movement): A change or movement for the sake of a thing to be what it is. For a seed, it might be an adult plant; for a sailboat, it might be sailing; for a ball at the top of a ramp, it might be coming to rest at the bottom.
- squibbles 6y ago
  I think if you remove consciousness from the equation, identifying causality would be a reasonable response to why. However, there is an underlying assumption that causality actually exists. In that case, I think the existence of causality would have to be taken as axiomatic.
stewbrew 6y ago
Hernan demonstrates how to put this to work in epidemiology. Quite interesting. Pearl's work should be seen in context though as a effort to create reasoning machines (AI), i.e., in automating reasoning.
kgwgk 6y ago
Nice Causal Inference book from Hernan and Robins (pdf available for download): https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ https://www.hsph.harvard.edu/miguel-hernan/causal-inference-...
petecooper 6y ago
Meta question: what's the workflow for creating PDFs like this? Is the PDF generated from markup-style text with a certain application or process? I notice a lot of academic papers have a particular 'feel' to them, using a subset of typography styles and presentational guidelines, especially where mathematical formulae are presented to the reader. Note: I do not have an especially academic background, nor any experience in university (tertiary) education. Please excuse my unintended ignorance on this subject. Thank you.
- vatican_banker 6y ago
  This is probably done with LaTeX or one of its variants See here: https://en.m.wikipedia.org/wiki/LaTeX https://en.m.wikipedia.org/wiki/LaTeX
- MperorM 6y ago
  Most STEM papers are written with latex, this book likely is too.
c0nsilience 6y ago
And with that, I just ordered a paperback copy on Amazon. Looking forward to reading this.
johndoe42377 6y ago
There is, again, principles from real philosophy, which state that there is and always will be a principal gap between an observation (and language) based model and What Is. The classic example is inability to tell why the sun will rise tomorrow by merely observations of it doing so and any kind of statistics. The "why" is in understanding of the nature of the process we call Sun and realization that such kind of process cannot be stopt or even change in 24 your. Another modern illustration of the same principle is the principal, infallible inability to infer the actual wiring if processor from the level of code it excites. This, by the way, is the very same Upanishadic principle of inability to infer the true nature of Brahman from the level of human intellect, conditioned by a language and experience. Some things will remain only guesses, models and "scientific", (or rather sectarian) consensus.
mwexler 6y ago
Just noting: paper (and book) came out in 2018. The book is well worth reading if you missed it then.

193 points by elcritch 6y ago

It's a great book; an enjoyable read even for someone like me who's mostly afraid of statistics. Pearl's enthusiasm for the topic is palpable, and he seems to have the academic pedigree to match the occasionally lofty claims. The book makes a very clear case in walking the layperson through various causal techniques and underlining how they differ from traditional methods. Few popular science books actually go into such detail.

tgb 6y ago
It's a good book, but some may be turned off by the first chapter or so that reads like he's trying to rub his victories into the faces of his academic adversaries. It had more than a little air of gloating to it, in my opinion, and the book would have been better without that. (Though he spent considerable time on the earlier precedents for his work, which I had previously thought was entirely novel.)

pjmorris 6y ago

For balance and discussion, Andrew Gelman's critical review of 'The Book of Why': https://statmodeling.stat.columbia.edu/2019/01/08/book-pearl-mackenzie/ https://statmodeling.stat.columbia.edu/2019/01/08/book-pearl...

epistasis 6y ago
Thanks for sharing this! I knew that Gelman and Pearl have been going back and forth for years, and it's great to see this. I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. That said, I think their disagreements don't have much effect on practice, and have as much to do with their different academic lineages as anything else. I expect that any dispute will be resolvable as the science progresses
- elcritch 6y ago
  > I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction. Are you able to elaborate?! It'd be really interesting to hear what/if tooling exists for these graphs. I just found the book recently and am listening to the Audible version. It also reaffirms my desire for a "Bayesian inference" spreadsheet like tool. Something that'd help organize a few dozen thoughts ideas for researchers/engineers.
  - currymj 6y ago
    R package "daggity" is excellent. http://www.dagitty.net/ http://www.dagitty.net/
  - epistasis 6y ago
    I've used libDAI in the past, but I'm not sure if it's maintained anymore. I've come across several other toolkits that looked interesting since then, but never had the right project. There's a group at (CMU?)/Pitt that I think now has a Python package for causal inference in particular. (Causality is far more complicated and harder to discover than a lot of the stuff I work towards though, which is all in biology.)
  - canjobear 6y ago
    > a "Bayesian inference" spreadsheet like tool http://probcomp.csail.mit.edu/software/bayesdb/ http://probcomp.csail.mit.edu/software/bayesdb/
  - sjg007 6y ago
    Thousands and millions of variables approaches deep learning w/ softmax. It is difficult to see how it would work any other way. I mean would you want to have a causal model of particles?
- lalaland1125 6y ago
  Why would Pearl's approach be better at scaling? If anything, the Rubin causal model scales wonderfully with more variables.
wavegeek 6y ago
This is a strange debate. They seem to be in violent agreement.
- anigbrowl 6y ago
  https://en.wikipedia.org/wiki/Narcissism_of_small_differences https://en.wikipedia.org/wiki/Narcissism_of_small_difference...
stewbrew 6y ago
Balance is the wrong word. you might want to check first who was AG's doctor father. Surprise, it was Rubin.

wavegeek 6y ago

Great book that clearly explains the perils or inferring causation and now you can deal with them constructively. For those who want more he has a more technical book "Causality: Models, Reasoning and Inference" which is also excellent. Pearl is a legend in the field, who wrote the seminal "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference". There is a school of thought that there is nothing new here. Pearl is very honest and open about who invented what and of the mistakes in his earlier work. While no one finding is entirely new here, the overall package adds up to a lot IMHO. And this after having intensively studied statistics and probability over many years. It really changed my approach to and understanding of causality. And most importantly it gave me reliable intuitions on the subject. After his books, things seem obvious to me that others struggle with.

zwaps 6y ago
I like Pearl's work a lot more than his opinions on who invented what, and who is right about what. For that reason, I would wholeheartedly recommend his books but I would also recommend skipping any sideshows about related literature. Fact is, Pearl selectively picks literature concerned with causality and, in particular, literature not successfully tackling the subject. He does ignore many other approaches to the issue, especially parallel developments to tackle problems in fields that he specifically critiques. In other words: Pearl, next to being a great researcher, is also a showman who knows how to build a following. The essence of the debate is this: Neither Pearl's framework, nor anyone else's capture all valid approaches to causal inference. One can construct cases in Rubin's framework that DAG can not solve and vice-versa. The downside to Pearl's approach is that it is - right now - more difficult to implement. The cases where DAG undoubtedly succeeds better than other approaches are, in a sense, unlikely to succeed as a practical research projects. That being said, a great strength of such graphical models is that they allow quite sophisticated reasoning in several well-known simple but non-intuitive cases. Such reasoning otherwise requires an immense amount of experience and / or education on the pitfalls of causal inference. That is also a reason why I would like to see this framework taught more in schools. All in all, as another great post in this thread has pointed out, much of the debate is in violent agreement on base issues. Once this issue transcends the egos involved, much progress will be made and that is, in my view, very exciting.

Anon84 6y ago

If you’re interested in learning more about causality, you might be interested in my blog series working through Pearls “Causal Inference in Statistics: A Primer”: https://github.com/DataForScience/Causality https://github.com/DataForScience/Causality (Jupiter notebooks and links to blog posts)

yashevde 6y ago

Thanks for sharing, this makes me want to move the book up my list of to-reads. As a side-note, I’m blown away to learn that Judea Pearl is the father of Daniel Pearl — can’t imagine the amount of composure it must take to continue to operate at the highest levels in his field after experiencing grief of that nature.

bachmeier 6y ago

Recognizing that not many will care about my opinion on the subject, I will link to this excellent (and IMO accurate) paper by Guido Imbens: https://arxiv.org/pdf/1907.07271.pdf https://arxiv.org/pdf/1907.07271.pdf One of the problems in groups is that there is a belief that the loudest and most self-confident individual is correct. I believe that is the case with Pearl. He attracts a following based on his personality, but when it comes to actually doing empirical research, he comes up short. "Separate from the theoretical merits of the two approaches, another reason for the lack of adoption in economics is that the DAG literature has not shown much evidence of the alleged benefits for empirical practice in settings that resonate with economists....In contrast in the DAG literature, TBOW, [Pearl, 2000], and[Peters, Janzing, and Sch ̈olkopf, 2017] have no substantive empirical examples, focusing largely on identification questions in what TBOW refers to as “toy” models. Compare the lack of impact of the DAG literature in economics with the recent embrace of regression discontinuity designs imported from the psychology literature, or with the current rapid spread of the machine learning methods from computer science, or the recent quick adoption of synthetic control methods developed in economics [Abadie and Gardeazabal, 2003, Abadie, Diamond, and Hainmueller,2010]. All three came with multiple concrete and detailed examples that highlighted their benefits over traditional methods. In the absence of such concrete examples the toy models in the DAG literature sometimes appear to be a set of solutions in search of problems, rather than a set of clever solutions for substantive problems previously posed in social sciences, bringing to mind the discussion of Leamer on the Tobit model ([Leamer, 1997])."

longtimegoogler 6y ago
That's a long paper and I am interested to read it. From what I have heard, there is more adoption of Pearl's methods in Epidemiology and in Social Sciences, in contrast with Econometrics. To me, it reminds me of Dyson Freeman's, "Birds and Frogs". For most Statistician's and Economists Regression is their goto tool and the Potential Outcome Framework is much more natural to them. You could say the same about the synthetic controls methods you mentioned.
squibbles 6y ago
I find this dialog to be reminiscent of the controversy surrounding Wolfram's A New Kind of Science. Perhaps one could frame it as the theorists versus the pragmatists.

rahimnathwani 6y ago

If you're interested in learning more about causal inference and 'do calculus', you might like this course and free textbook: https://www.bradyneal.com/causal-inference-course https://www.bradyneal.com/causal-inference-course

transfire 6y ago

"Why" is not a query of causality, but rather purpose. The proper word would be "How".

kgwgk 6y ago
https://en.wikipedia.org/wiki/Four_causes https://en.wikipedia.org/wiki/Four_causes Aristotle holds that there are four kinds of answers to "why" questions: Matter (the material cause of a change or movement): The aspect of the change or movement that is determined by the material that composes the moving or changing things. For a table, such might be wood; for a statue, such might be bronze or marble. Form (the formal cause of a change or movement): A change or movement caused by the arrangement, shape, or appearance of the thing changing or moving. Aristotle says, for example, that the ratio 2:1, and number in general, is the cause of the octave. Agent (the efficient or moving cause of a change or movement): Consists of things apart from the thing being changed or moved, which interact so as to be an agency of the change or movement. For example, the efficient cause of a table is a carpenter, or a person working as one, and according to Aristotle the efficient cause of a boy is a father. End or purpose (the final cause of a change or movement): A change or movement for the sake of a thing to be what it is. For a seed, it might be an adult plant; for a sailboat, it might be sailing; for a ball at the top of a ramp, it might be coming to rest at the bottom.
squibbles 6y ago
I think if you remove consciousness from the equation, identifying causality would be a reasonable response to why. However, there is an underlying assumption that causality actually exists. In that case, I think the existence of causality would have to be taken as axiomatic.

stewbrew 6y ago

Hernan demonstrates how to put this to work in epidemiology. Quite interesting. Pearl's work should be seen in context though as a effort to create reasoning machines (AI), i.e., in automating reasoning.

kgwgk 6y ago

Nice Causal Inference book from Hernan and Robins (pdf available for download): https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ https://www.hsph.harvard.edu/miguel-hernan/causal-inference-...

petecooper 6y ago

Meta question: what's the workflow for creating PDFs like this? Is the PDF generated from markup-style text with a certain application or process? I notice a lot of academic papers have a particular 'feel' to them, using a subset of typography styles and presentational guidelines, especially where mathematical formulae are presented to the reader. Note: I do not have an especially academic background, nor any experience in university (tertiary) education. Please excuse my unintended ignorance on this subject. Thank you.

vatican_banker 6y ago
This is probably done with LaTeX or one of its variants See here: https://en.m.wikipedia.org/wiki/LaTeX https://en.m.wikipedia.org/wiki/LaTeX
MperorM 6y ago
Most STEM papers are written with latex, this book likely is too.

c0nsilience 6y ago

And with that, I just ordered a paperback copy on Amazon. Looking forward to reading this.

johndoe42377 6y ago

There is, again, principles from real philosophy, which state that there is and always will be a principal gap between an observation (and language) based model and What Is. The classic example is inability to tell why the sun will rise tomorrow by merely observations of it doing so and any kind of statistics. The "why" is in understanding of the nature of the process we call Sun and realization that such kind of process cannot be stopt or even change in 24 your. Another modern illustration of the same principle is the principal, infallible inability to infer the actual wiring if processor from the level of code it excites. This, by the way, is the very same Upanishadic principle of inability to infer the true nature of Brahman from the level of human intellect, conditioned by a language and experience. Some things will remain only guesses, models and "scientific", (or rather sectarian) consensus.

mwexler 6y ago

Just noting: paper (and book) came out in 2018. The book is well worth reading if you missed it then.