
Causal Inference in Statistics: A Primer - dstein64
http://bayes.cs.ucla.edu/PRIMER/
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pavpanchekha
Judea Pearl's work on causality is some of the most important statistics work
that is happening these days. We've known how to do statistics to find
correlations and make inferences, but he put _causality_ on a firm
mathematical basis, and discovered fascinating statistics as he did. This book
should be a blast.

~~~
fenomas
Can you recommend any casual (article-sized) reading about this? Sounds really
interesting!

Edit: xtacy's post answers me entirely.

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mazr
For a quick and general purpose introduction on Causality, the Epilogue of a
former book of Pearl is great : "The Art and Science of Cause and Effect"
[http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf](http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf)

~~~
fenomas
Thank you! This rather blew my mind and I hope others take time to try it out.

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ced
I was looking for a review, and found Gelman's recommendation of

 _Causal Inference for Statistics, Social, and Biomedical Sciences_

and mention of this book: [http://www.hsph.harvard.edu/miguel-hernan/causal-
inference-b...](http://www.hsph.harvard.edu/miguel-hernan/causal-inference-
book/)

Does anyone have a comment on those? I've read Pearl's two earlier books, and
found the one on causality quite hard to navigate. The basic ideas are cool,
but it's hard to connect the more advanced theorems with anything I could
actually implement.

~~~
xtacy
I too found Pearl's book hard to navigate on first attempt. Do not let that
stop you! After a hiatus, I stumbled upon this blog post [1], which explained
the core ideas in Pearl's framework beautifully in a simple language. My
advice is to persist, fill any holes in fundamentals (mostly basic
probability), and persist. After working out the examples in the blog post on
paper and contrasting it to other ideas out there (potential outcome
framework), it became quite clear what Pearl was trying to articulate.

Pearl is also an enthusiastic speaker. You can search for his talks online at
various venues (Stanford, Microsoft Research, etc.) to learn more.

[1] [http://www.michaelnielsen.org/ddi/if-correlation-doesnt-
impl...](http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-
causation-then-what-does/)

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le0n
Just so people know, there is a competing/complementary approach to causality
in statistics, called the potential outcomes or (Neyman-)Rubin causal model,
which as I understand it is currently more popular than Pearl's graphical/do-
calculus approach.

~~~
TheLogothete
There is also TS related body of knowledge about causal impact using the
notion of counterfactuals. Google has sponsored research in the field [1] and
also released an R package [2].

[1]
[http://research.google.com/pubs/pub41854.html](http://research.google.com/pubs/pub41854.html)

[2]
[https://google.github.io/CausalImpact/CausalImpact.html](https://google.github.io/CausalImpact/CausalImpact.html)

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dschiptsov
Philosophy 101 would tell us that statistics could capture only correlations,
not causation. Causation require different kind of knowledge, of what is
beyond appearances.

~~~
warrenpj
When we see that two events are correlated (which we need some kind of
statistics to do), we can tell a story (a theory, or an explanation) about how
one event causes the other. If the explanation stands up to rational testing
over time (where statistics are an important tool), then we have gained
knowledge - one plausible explanation of what is "beyond appearances".

Therefore statistics are useful both before positing an explanation, and after
to falsify it.

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dschiptsov
That theory or explanation requires the domain knowledge I am talking about.

Mere statistics about appearances is not enough.

To make it clear - statistics is obviously useful. It just cannot infer any
proposition like x is y for all values of x.

~~~
warrenpj
I agree. I should have said that explicitly, before.

