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Causal Inference in Statistics: A Primer
21–29 of 29 posts
Re: Causal Inference in Statistics: A Primer
#22Judea 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 foun…
They're different questions to some extent.
In the statistical literature on causality, the counterfactual definition is almost universally accepted: if you do X then Y happens, but if you wouldn't have done X, Y wouldn't have happened. So the main challenge that statisticians are tackling is not the ontological question of whether causality can really be said to exist in the world, but rather the practical question of how to make measurements performed at different times, in different places, on different people, using different machinery, as comparable as possible.
Re: Causal Inference in Statistics: A Primer
#23I 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 studied the Hernan/Robins book for a course on causal inference, and I love it. But frankly it's a pretty niche topic, and so for the non-statisticians here on HN who are trying to get better at statistics, just keep in mind that there are so many other topics you probably want to tackle first. Many of the concerns addressed in the Hernan/Robins book probably wouldn't even make sense to you if you didn't have a firm statistical background already.
Gelman/Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models or Angrist/Pischke's Mastering 'Metrics embed ideas and techniques from causal inference into the broader context of regression modeling which makes these books more immediately useful. Those would probably be my two recommendations for non-statisticians.
Re: Causal Inference in Statistics: A Primer
#24Judea 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
I am not sure how that can be called well considered. He really shouldn't throw stones in glass houses. It is clearly a pro-Israel piece, he even alludes to the bulldozing regime as being acceptable without directly saying it. Shameful to use his sons death for this propaganda.
Re: Causal Inference in Statistics: A Primer
#25Earlier quoted context omitted.
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. Th…
Would you like to elaborate about "somewhat wrong", with quotations from Principles of Mathematics, for example?
It's "somewhat wrong" because in some cases it is possible to derive causation using statistical methods.
Have you read the linked book? It should answer your questions. If not I'll point you to Michael Nielsen's post[1], where he explain[s] how the causal calculus can sometimes (but not always!) be used to infer causation from a set of data, even when a randomized controlled experiment is not possible. Also in the post, I’ll describe some of the limits of the causal calculus
It's a pretty long post, but the gist of it is that in some circumstances it's possible to build a world model of an imaginary controlled, randomized experiment and then see if non-controlled, real world data matches those expectations.
What that gives you is a distribution of the probabilities of causality.
[1] http://www.michaelnielsen.org/ddi/if-correlation-doesnt-impl...
Re: Causal Inference in Statistics: A Primer
#26Just 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.
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].
Re: Causal Inference in Statistics: A Primer
#27Earlier quoted context omitted.
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
Thank you! This rather blew my mind and I hope others take time to try it out.
Re: Causal Inference in Statistics: A Primer
#28Earlier quoted context omitted.
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. Th…
Would you like to elaborate about "somewhat wrong", with quotations from Principles of Mathematics, for example? It's "somewhat wrong" because in some cases it is possible to derive causation using statistical methods. Have you read the linked book? It should answer your questions. If not I'll point you to Michael Nielsen's post[1], where he explain[s] how the causal calculus can sometimes (but not always!) be used t…
The probability calculus has nothing to do with correctness of probabilities supplied the very same way the propositional calculus had nothing to do with validity of given propositions. Or any calculus in that matter.
Inference is application of valid heuristics. Mere statistics is not sufficient.
Re: Causal Inference in Statistics: A Primer
#29Earlier quoted context omitted.
His WSJ article about the murder of his son (WSJ journalist Daniel Pearl) was well-considered, too: http://online.wsj.com/article/SB123362422088941893.html
I am not sure how that can be called well considered. He really shouldn't throw stones in glass houses. It is clearly a pro-Israel piece, he even alludes to the bulldozing regime as being acceptable without directly saying it. Shameful to use his sons death for this propaganda.
> Shameful to use his sons death for this propaganda.
I don't have strong feelings towards his views, but I don't see how it can be called propaganda. He presents a reasoned argument, in contrast to the violence that was visited upon his child. I consider that admirable.