6 ms·

Review: The Book of Why

148 points by pizzicato 6y ago

breck 6y ago
I like the "Ladder of causation": Rung 1: Associations, observational data (seeing) Rung 2: Intervention (doing) Rung 3: Counterfactuals (imagining) I often go in reverse order—let's figure out the cheapest clever ways to prove ship will sink (imagining). Then if it seems like it might float let's build it and throw it on the pond (doing). Then if it seems to float let's hop on board and see what happens.
kenjackson 6y ago
I feel like I’ve tried to read several writings on this topic, mostly by Pearl. I feel like I’m good during the intro and motivation, but once it gets to the meat I’m completely lost. I feel like this is an area that would provide rich value if I could ever understand it.
neatze 6y ago
Does Judea Pearl other books overlap with The Book of Why ?
- michelpp 6y ago
  Yes. To me The Book of Why is sort of an approachable summary of his whole career culminating in his work on causal inference.
michelpp 6y ago
Brady Neal has a great video course on the subject with slides and readings including Pearl and others: https://www.bradyneal.com/causal-inference-course https://www.bradyneal.com/causal-inference-course EDIT: I should add Brady also publishes his course textbook online, and it's less Pearl-centric than The Book of Why but still covers the complete subject and then some: https://www.bradyneal.com/causal-inference-course#course-textbook https://www.bradyneal.com/causal-inference-course#course-tex...
- victor106 6y ago
  Awesome!!! Thanks so much for this.
- zyngaro 6y ago
  Thank you for the link
bachmeier 6y ago
> This book dwells on the history of statistics a lot, and statisticians, as the authors would have you believe, are zealots who have conspired to keep causal thinking out of their field right from the start. That is, until Pearl instigated the "Causal Revolution", as he dubs it, the latest and greatest gift to modern science. I have no dog in this fight, but Pearl (whom I assume is the source of most of these opinions put to paper by Mackenzie) often comes across as wildly biased and grandiose. For what it's worth, I doubt that statisticians as a whole are anywhere as malicious or ignorant as they're portrayed in this book. This is correct AFAICT (I'm not a statistician even though I read a lot of the statistics literature). The strange thing is that I've never seen any obvious benefits to his comments of this nature. In the most generous possible reading, they are a distraction, with a less generous reading being that you can't trust his interpretation of anything.
- auggierose 6y ago
  Well, I read the book. I like his clear style, he tries to get across what is different about his approach. To be honest, I like probability theory, but never thought much of statistics, so I guess I can sympathise from where he is coming from. I enjoyed this book much more than any text about statistics I ever read.
  - bluefox 6y ago
    I've read this book twice. The first time, I enjoyed it, and as I read it I felt that I understood the gist, at least intuitively. Similar to what you describe, my view was always that statistics is a bunch of tricks, and probability is much deeper. After reading this book, I read another book, a technical book about probabilistic graphical models. As I read the book, I implemented most of the algorithms. I also had to read a bunch of papers from the 80s and 90s to do that. I then decided to read this book a second time, and now I really came to appreciate Pearl's points, and can see why statistics (and probability...) are insufficient, and the need for his do-calculus. I've also been reading much of his 1988 classic, though not done with it. While I'm not there yet (still implementing more papers, and not read his Causality book yet) I can see how his proposed calculus and the work of his students in that area can help do the things he describes in the last chapter. So, the book can be interesting to lay people, and it may entice them to learn more. I think this is the book's purpose, and therefore that it is a success, at least with me.
pavlov 6y ago
> "This book dwells on the history of statistics a lot, and statisticians, as the authors would have you believe, are zealots who have conspired to keep causal thinking out of their field right from the start." I've read the book and have no dog in the fight. IMO this is an uncharitable interpretation of Pearl's position. The authors of the book present the work of many past statisticians on both sides of the causal debate. A few influential people are indeed rendered as almost caricatures, but clearly that doesn't represent the entire field when the authors also dive deeply into the work of other statisticians who explored causality.
- robwwilliams 6y ago
  Agree with this counterpoint. Sewall Wright for one (and his father) are given great credit. It is RA Fisher who comes in for well deserved flak for his infamous obstinance.
- bart_spoon 6y ago
  In multiple places Pearl castigated the entire field of statistics, in addition to outright character assassinations of long dead statisticians. I think the original author’s interpretation is perhaps too charitable.
haberman 6y ago
> The catch is that, whether explicitly or implicitly, you must make assumptions in the first place about the directions of causality among the variables. That right there is the headline to me. Compare this with the blurb in the dust jacket of the book: > "Correlation is not causation." This mantra, espoused by scientists for more than a century, led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, led by AI researcher Judea Pearl and his colleagues, has cut through years of confusion about the nature of knowledge and established the study of causality at the center of scientific inquiry. This all but claims that these new causal tools have found a solution to the problem of "correlation is not causation." But they have done no such thing: there is no new technique offered here for establishing causation in any better or easier way than the RCT of yore. If you get rid of the warning "correlation is not causation" and focus everyone's attention on all the exciting inferences you can make when you assume causation, I'm worried that the end result is a lot of bad science.
- spekcular 6y ago
  For more on this, you might enjoy this survey of Pearl's work, and its relevance to economics research, by Guido Imbens: https://arxiv.org/abs/1907.07271 https://arxiv.org/abs/1907.07271. It is long, but through. The conclusion is essentially that Pearl is over-hyped, though my summary does significant violence to the nuances of his argument.
  - littlestymaar 6y ago
    Using «the book of Why», which is a book of popular science, not an academic book, as a reference is a bit troubling though.
    - spekcular 6y ago
      A significant number of Pearl's works are cited, not just that one. (See the references section.)
- littlestymaar 6y ago
  > all the exciting inferences you can make when you assume causation, I'm worried that the end result is a lot of bad science. But this, is the definition of science: you make models on top of hypothesis that you assume, you see how existing data fits your model, and then you make falsifiable predictions based on your model. Studying data without any (causal) model of what's happening is just collecting statistically significant trivia. It is research, for sure, but that's not enough to make it science. But hey, at least you published something.
Anon84 6y ago
You might enjoy my blog series on Causality where I work through Pearls 'Causal Inference in Statistics: A Primer' using Python: https://github.com/DataForScience/Causality https://github.com/DataForScience/Causality </ShamelessSelfPromotion>
- michelpp 6y ago
  Wow what an amazing work. Thank you!
  - Anon84 6y ago
    Thank you! The only way for me to learn anything is to work through it so this time around I decided to make my work public. I’m glad you found it useful.
- ZephyrBlu 6y ago
  I have skimmed the first 2 chapters and I still don't understand how causal inference is a useful tool or how you apply it to anything moderately complex. Obviously being able to identify causality is useful, but I don't understand how you can apply causal inference and get a meaningful result. To apply any of the rules regarding DAG structure you first have to have a DAG of events, which seems like it would be difficult to accurately build up.
ta988 6y ago
I highly recommend you to start with that one if you are new to causality. It is much more approachable than his other books.
rkabra 6y ago
I did a data scientist-focused review on the same book if that's useful: https://medium.com/@rishabhkabra/book-review-the-book-of-why-by-judea-pearl-dana-mackenzie-44a87f71ad45 https://medium.com/@rishabhkabra/book-review-the-book-of-why...
marttt 6y ago
Judea Pearl's home page for the book might also be worth posting here: http://bayes.cs.ucla.edu/WHY/ http://bayes.cs.ucla.edu/WHY/
albertTJames 6y ago
As asked by the author, here is an example of application in medicine: https://www.nature.com/articles/s41467-020-17419-7 https://www.nature.com/articles/s41467-020-17419-7
kgwgk 6y ago
Another good book on the subject. Freely available. Causal Inference: What If Miguel A. Hernan and James M. Robins https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ https://www.hsph.harvard.edu/miguel-hernan/causal-inference-... https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2021/01/ciwhatif_hernanrobins_31jan21.pdf https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2...
deleted 6y ago
[deleted]

148 points by pizzicato 6y ago

I like the "Ladder of causation": Rung 1: Associations, observational data (seeing) Rung 2: Intervention (doing) Rung 3: Counterfactuals (imagining) I often go in reverse order—let's figure out the cheapest clever ways to prove ship will sink (imagining). Then if it seems like it might float let's build it and throw it on the pond (doing). Then if it seems to float let's hop on board and see what happens.

kenjackson 6y ago

I feel like I’ve tried to read several writings on this topic, mostly by Pearl. I feel like I’m good during the intro and motivation, but once it gets to the meat I’m completely lost. I feel like this is an area that would provide rich value if I could ever understand it.

neatze 6y ago

Does Judea Pearl other books overlap with The Book of Why ?

michelpp 6y ago
Yes. To me The Book of Why is sort of an approachable summary of his whole career culminating in his work on causal inference.

michelpp 6y ago

Brady Neal has a great video course on the subject with slides and readings including Pearl and others: https://www.bradyneal.com/causal-inference-course https://www.bradyneal.com/causal-inference-course EDIT: I should add Brady also publishes his course textbook online, and it's less Pearl-centric than The Book of Why but still covers the complete subject and then some: https://www.bradyneal.com/causal-inference-course#course-textbook https://www.bradyneal.com/causal-inference-course#course-tex...

victor106 6y ago
Awesome!!! Thanks so much for this.
zyngaro 6y ago
Thank you for the link

bachmeier 6y ago

> This book dwells on the history of statistics a lot, and statisticians, as the authors would have you believe, are zealots who have conspired to keep causal thinking out of their field right from the start. That is, until Pearl instigated the "Causal Revolution", as he dubs it, the latest and greatest gift to modern science. I have no dog in this fight, but Pearl (whom I assume is the source of most of these opinions put to paper by Mackenzie) often comes across as wildly biased and grandiose. For what it's worth, I doubt that statisticians as a whole are anywhere as malicious or ignorant as they're portrayed in this book. This is correct AFAICT (I'm not a statistician even though I read a lot of the statistics literature). The strange thing is that I've never seen any obvious benefits to his comments of this nature. In the most generous possible reading, they are a distraction, with a less generous reading being that you can't trust his interpretation of anything.

auggierose 6y ago
Well, I read the book. I like his clear style, he tries to get across what is different about his approach. To be honest, I like probability theory, but never thought much of statistics, so I guess I can sympathise from where he is coming from. I enjoyed this book much more than any text about statistics I ever read.
- bluefox 6y ago
  I've read this book twice. The first time, I enjoyed it, and as I read it I felt that I understood the gist, at least intuitively. Similar to what you describe, my view was always that statistics is a bunch of tricks, and probability is much deeper. After reading this book, I read another book, a technical book about probabilistic graphical models. As I read the book, I implemented most of the algorithms. I also had to read a bunch of papers from the 80s and 90s to do that. I then decided to read this book a second time, and now I really came to appreciate Pearl's points, and can see why statistics (and probability...) are insufficient, and the need for his do-calculus. I've also been reading much of his 1988 classic, though not done with it. While I'm not there yet (still implementing more papers, and not read his Causality book yet) I can see how his proposed calculus and the work of his students in that area can help do the things he describes in the last chapter. So, the book can be interesting to lay people, and it may entice them to learn more. I think this is the book's purpose, and therefore that it is a success, at least with me.

pavlov 6y ago

> "This book dwells on the history of statistics a lot, and statisticians, as the authors would have you believe, are zealots who have conspired to keep causal thinking out of their field right from the start." I've read the book and have no dog in the fight. IMO this is an uncharitable interpretation of Pearl's position. The authors of the book present the work of many past statisticians on both sides of the causal debate. A few influential people are indeed rendered as almost caricatures, but clearly that doesn't represent the entire field when the authors also dive deeply into the work of other statisticians who explored causality.

robwwilliams 6y ago
Agree with this counterpoint. Sewall Wright for one (and his father) are given great credit. It is RA Fisher who comes in for well deserved flak for his infamous obstinance.
bart_spoon 6y ago
In multiple places Pearl castigated the entire field of statistics, in addition to outright character assassinations of long dead statisticians. I think the original author’s interpretation is perhaps too charitable.

haberman 6y ago

> The catch is that, whether explicitly or implicitly, you must make assumptions in the first place about the directions of causality among the variables. That right there is the headline to me. Compare this with the blurb in the dust jacket of the book: > "Correlation is not causation." This mantra, espoused by scientists for more than a century, led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, led by AI researcher Judea Pearl and his colleagues, has cut through years of confusion about the nature of knowledge and established the study of causality at the center of scientific inquiry. This all but claims that these new causal tools have found a solution to the problem of "correlation is not causation." But they have done no such thing: there is no new technique offered here for establishing causation in any better or easier way than the RCT of yore. If you get rid of the warning "correlation is not causation" and focus everyone's attention on all the exciting inferences you can make when you assume causation, I'm worried that the end result is a lot of bad science.

spekcular 6y ago
For more on this, you might enjoy this survey of Pearl's work, and its relevance to economics research, by Guido Imbens: https://arxiv.org/abs/1907.07271 https://arxiv.org/abs/1907.07271. It is long, but through. The conclusion is essentially that Pearl is over-hyped, though my summary does significant violence to the nuances of his argument.
- littlestymaar 6y ago
  Using «the book of Why», which is a book of popular science, not an academic book, as a reference is a bit troubling though.
  - spekcular 6y ago
    A significant number of Pearl's works are cited, not just that one. (See the references section.)
littlestymaar 6y ago
> all the exciting inferences you can make when you assume causation, I'm worried that the end result is a lot of bad science. But this, is the definition of science: you make models on top of hypothesis that you assume, you see how existing data fits your model, and then you make falsifiable predictions based on your model. Studying data without any (causal) model of what's happening is just collecting statistically significant trivia. It is research, for sure, but that's not enough to make it science. But hey, at least you published something.

Anon84 6y ago

You might enjoy my blog series on Causality where I work through Pearls 'Causal Inference in Statistics: A Primer' using Python: https://github.com/DataForScience/Causality https://github.com/DataForScience/Causality </ShamelessSelfPromotion>

michelpp 6y ago
Wow what an amazing work. Thank you!
- Anon84 6y ago
  Thank you! The only way for me to learn anything is to work through it so this time around I decided to make my work public. I’m glad you found it useful.
ZephyrBlu 6y ago
I have skimmed the first 2 chapters and I still don't understand how causal inference is a useful tool or how you apply it to anything moderately complex. Obviously being able to identify causality is useful, but I don't understand how you can apply causal inference and get a meaningful result. To apply any of the rules regarding DAG structure you first have to have a DAG of events, which seems like it would be difficult to accurately build up.

ta988 6y ago

I highly recommend you to start with that one if you are new to causality. It is much more approachable than his other books.

rkabra 6y ago

I did a data scientist-focused review on the same book if that's useful: https://medium.com/@rishabhkabra/book-review-the-book-of-why-by-judea-pearl-dana-mackenzie-44a87f71ad45 https://medium.com/@rishabhkabra/book-review-the-book-of-why...

marttt 6y ago

Judea Pearl's home page for the book might also be worth posting here: http://bayes.cs.ucla.edu/WHY/ http://bayes.cs.ucla.edu/WHY/

albertTJames 6y ago

As asked by the author, here is an example of application in medicine: https://www.nature.com/articles/s41467-020-17419-7 https://www.nature.com/articles/s41467-020-17419-7

kgwgk 6y ago

Another good book on the subject. Freely available. Causal Inference: What If Miguel A. Hernan and James M. Robins https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ https://www.hsph.harvard.edu/miguel-hernan/causal-inference-... https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2021/01/ciwhatif_hernanrobins_31jan21.pdf https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2...

deleted 6y ago

[deleted]