
Engines of Evidence – A Conversation with Judea Pearl - clumsysmurf
https://www.edge.org/conversation/judea_pearl-engines-of-evidence
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jasoncrawford
See also this introduction to Pearl's causal calculus by Michael Nielsen:

If Correlation Doesn’t Imply Causation, then What Does?
[http://www.michaelnielsen.org/ddi/if-correlation-doesnt-
impl...](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-
causation-then-what-does/)

~~~
shoo
This is a great introductory article, I heartily recommend it.

I read this myself few years ago, got very excited, and had a go at writing a
program that could automatically perform the derivation from Nielsen's worked
example:

[https://github.com/fcostin/d_separation](https://github.com/fcostin/d_separation)

This was really interesting to work on, but I guess I became disinterested in
it after I got it to crudely work. Be aware that the quality of the code,
documentation, and the entire idea of the "proof-search" aspect of it is
probably not very good.

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lordnacho
I came across Causality via this LessWrong article a few years ago. Seemed to
make sense, but it is of course a very deep subject.

[http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model...](http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/)

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dirtyaura
Pearl's "Probabilistic Reasoning in Intelligent Systems" was one of the most
lauded books during my time in the university. I remember when Pearl gave a
talk about his upcoming book Causality, my professor said that it might become
one of the most important math and philosophy books of our life time. I never
read Causality, and it is now 16 years since the publication. Can anyone who
follows the field comment how it is perceived today?

~~~
gabrielgoh
Im not completely sold on his book being a textbook.

It is incredible bit of research, I agree, but it still has rough edges and
should be treated as such, a work in progress. I do not think this book should
be the bible of causality, and I think we're worse off for thinking it is.

Personally, I find the work of Richardson and Robins in Single World
Intervention Graphs
[https://www.csss.washington.edu/Papers/wp128.pdf](https://www.csss.washington.edu/Papers/wp128.pdf)
far more intuitive and compelling than Pearl's do-calculus. The notation is
too slightly cleaner and it does away mostly with the unorthodox notation
Pearl uses.

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s_ngularity
I would like to get an introduction to the main ideas in current causality
research. Is Pearl's book still the best introduction, or would you recommend
something else instead?

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johnmyleswhite
I think most people who study causal inference would say that Pearl's writing
is seldom as clear as a person unfamiliar with his ideas would like it to be.
These days, I suspect most people would benefit from starting with Morgan and
Winship instead: [https://www.amazon.com/Counterfactuals-Causal-Inference-
Prin...](https://www.amazon.com/Counterfactuals-Causal-Inference-Principles-
Analytical/dp/0521671930)

In addition to presenting Pearl's ideas more clearly than Pearl himself tends
to do, Morgan and Winship also describe Rubin's work more fairly than Pearl
does. (On the other hand, Rubin also tends to disparage Pearl's work more than
is necessary.)

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chrismealy
I tried reading Pearl's "Causality" but it was too much for me. Anybody get
through it? What did you think?

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MrQuincle
There will be a popular science book from him coming out, I read a draft a
while ago. It will present it all in a digestible way.

The do-calculus to me looks a bit cumbersome. I'm not generally attracted to
math that doesn't look like math, except if it is nice looking. :-)

On a more general level, when I'm building robots, I consider the interface
between their minds and the world a Markov blanket. This means that state in
the world can only be represented in the robot by learning through its
interface by a combination of forward and inverse models. The existence of a
physical barrier makes it possible to define information going through it. You
might suggest that the world is created by the robot's mind, but that is
probably difficult to back with any information criterion.

If the robot learns its body, it knows that it is itself who is performing a
movement or if it is being pushed. Knowing that it was itself the actor is
enough to know the direction of the causal arrow.

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tr352
Can you tell me the name of that book that is coming out?

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MrQuincle
[https://books.google.nl/books/about/The_Book_of_Why.html?id=...](https://books.google.nl/books/about/The_Book_of_Why.html?id=9H0dDQAAQBAJ&redir_esc=y)

I'm not sure if that's gonna be the final title though.

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Caligula
Just an interesting note, his son was Daniel Pearl, who was tragically
murdered in Pakistan.

I enjoyed Probabilistic Reasoning in Intelligent systems. I was unaware of
Causality.

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szemet
A good link maybe: [http://plato.stanford.edu/entries/causation-
mani/](http://plato.stanford.edu/entries/causation-mani/)

Pearl's theory summarized in chapter 5, but it is put into context: related
work, criticism, etc...

