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Causal Inference in Statistics: A Primer

bayes.cs.ucla.edu

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Re: Causal Inference in Statistics: A Primer

#11

Post by Fomite »

ced wrote: post #2

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... 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 th…

I'm a very big fan of Miguel Hernan's work, and have found him fairly approachable - he's made a decent stab at taking some of the more opaque bits of epidemiology methods and making them more clear.

Re: Causal Inference in Statistics: A Primer

#12

Post by fenomas »

pavpanchekha wrote: post #3

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.

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

Edit: xtacy's post answers me entirely.

Re: Causal Inference in Statistics: A Primer

#13

Post by stalaie »

fenomas wrote: post #12
pavpanchekha wrote: post #3

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.

Can you recommend any casual (article-sized) reading about this? Sounds really interesting! Edit: xtacy's post answers me entirely.

Check out his turing award lecture: http://amturing.acm.org/vp/pearl_2658896.cfm

Re: Causal Inference in Statistics: A Primer

#14

Post by nl »

pavpanchekha wrote: post #3

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.

His WSJ article about the murder of his son (WSJ journalist Daniel Pearl) was well-considered, too: http://online.wsj.com/article/SB123362422088941893.html

Re: Causal Inference in Statistics: A Primer

#15

Post by nl »

dschiptsov wrote: post #4

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

I just unflagged this comment. While it's actually somewhat wrong, it makes an important point.

Statistics can be used to discover a causal relationship. It can't give you an absolute answer, but it can give you a statistical likelihood of the causality. That's a pretty important step forward.

That's what this is about.

Re: Causal Inference in Statistics: A Primer

#16

Post by pmoriarty »

pavpanchekha wrote: post #3

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.

Ever since David Hume[1], a couple of hundred years ago (and arguably long before him), causality has been recognized to stand on very shaky philosophical grounds. It will be interesting to learn what statisticians can make of it. Even if there is in fact a firm mathematical foundation on which causality can rest, that in itself is problematic because mathematicians themselves have mostly given up trying to find foundations for mathematics.

[1] - https://en.wikipedia.org/wiki/David_Hume

Re: Causal Inference in Statistics: A Primer

#17

Post by Houshalter »

dschiptsov wrote: post #4

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

It actually is possible to infer causality from statistics. That's (partially) what this author's work is all about. For a brief explanation of how that's possible, see this post: http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_model...

Re: Causal Inference in Statistics: A Primer

#18

Post by 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.

Re: Causal Inference in Statistics: A Primer

#19

Post by mazr »

fenomas wrote: post #12
pavpanchekha wrote: post #3

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.

Can you recommend any casual (article-sized) reading about this? Sounds really interesting! Edit: xtacy's post answers me entirely.

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

Re: Causal Inference in Statistics: A Primer

#20

Post by dschiptsov »

nl wrote: post #15
dschiptsov wrote: post #4

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

I just unflagged this comment. While it's actually somewhat wrong, it makes an important point. Statistics can be used to discover a causal relationship. It can't give you an absolute answer, but it can give you a statistical likelihood of the causality. That's a pretty important step forward. That's what this is about.

Likehood or probability makes sense only in models where one knows with maximum certainty that he have captured all/every relevant variables, its weights and has all kinds of possible events in a distribution. Otherwise the whole model is mere a story. An illusion. Failure to capture reality adequately is where so-called "black swans" are coming from.

This is meaning behind the "correlation is not causation" meme. There is nothing wrong with Bayesian reasoning, except when it is applyed to an inadequate dataset, which is almost always the case.

Would you like to elaborate about "somewhat wrong", with quotations from Principles of Mathematics, for example?

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