61–70 of 72 posts
> what does? (1) A clear mechanism. Data. My car won't run. Cause. The universal joint at the differential for the rear wheels failed leaving the rear end of the drive shaft on the ground. (2) A solid scientific theory. Data. I let go of the 2 x 4, and it hit my foot and hurt. Cause. Newton's law of gravity. (3) Other. Data. There is a correlation between smoking and lung cancer. Cause. Guess that there are some chem…
Sometime ago I tried to come up with the simplest possible explanation for Simpson's paradox. This was the result: 1) Imagine that most women with a certain disease survive, while most men die. 2) Imagine that most women with the disease take a certain medicine, while most men don't. 3) Imagine that the medicine has absolutely no effect. Women just happen to have better innate resistance to the disease, and also just…
This is seriously in need of a tl;dr
I've read the book, a scattered 2 times and couldn't give you a tl;dr.
My recollection is that this framework of causal inference allows one to ask questions about a probabilistic model that one can then try and measure to test causality.
These questions are interventions or assertions (the do operators) that something happened.
So one would start out with a Graphical Model like in the smoking example which defines a probability model, and then make do assertions on the model for various candidate causes and see what that would imply about the change in probabilities and then design an experiment to measure them and confirm them.
Earlier quoted context omitted.
Where do hypotheses come from if correlation isn't weak evidence for causation?
Usually, you have some additional information, aside from the correlation itself. For example, instead of just knowing "some variable X correlates with some variable Y", you may know what X and Y actually are, and some facts about similar entities.
Good reading in addition to Pearl himself is via Cosma Shalizi. Ref list: http://vserver1.cscs.lsa.umich.edu/~crshalizi/notebooks/caus... Chapters: http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch22.pdf http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch23.pdf http://www.stat.cmu.edu/~cshalizi/uADA/13/lectures/ch24.pdf Incidentally, Shalizi is a great source for going back to the basics. His course at CMU "Adv…
> what does? (1) A clear mechanism. Data. My car won't run. Cause. The universal joint at the differential for the rear wheels failed leaving the rear end of the drive shaft on the ground. (2) A solid scientific theory. Data. I let go of the 2 x 4, and it hit my foot and hurt. Cause. Newton's law of gravity. (3) Other. Data. There is a correlation between smoking and lung cancer. Cause. Guess that there are some chem…
Doesn't sound like you understand what the discussion is about.
> If correlation doesn’t imply causation, then what does?
not directly to the article or the discussion here on HN.
I don't think that the article makes much sense.
I gave a very simple answer, (1) and (2). For (3), that's a mess and closer to the discussion.
My view is that for causality, what I gave with (1) and (2), simple, childishly simple, dirt simple, is, unfortunately, in reality, about all there is to the poor, struggling subject.
Put more respectfully, beyond my simplistic (1) and (2), working with causality is super difficult and quite unpromising as in mostly just f'get about it. For my (3) it boils down to ways to reject causality and then we accept causality once we get so tired trying to reject it we just give up and accept it. Why? Because without something detailed and mechanical, say, from chemistry, biochemistry, and the forbiddingly complicated, detailed biochemistry of cells, we are missing anything very solid to call causal. E.g., in my (1), with the universal joint that failed, we have a simple explanation that makes a solid cause; getting something so simple and solid for a cause of cancer from smoking will be super difficult. Don't worry, I don't smoke, but neither do I claim really to know a cause of cancer.
Causality is a great, intuitive idea for humans and animals, but looked at in detail it's tough to establish in all but some narrow situations.
For causal networks, path analysis, Markov random fields, directed acyclic graphs, lots of diagrams with circles and arrows, f'get about it.
For getting causality out of data analysis, mostly just a fool's errand -- f'get about it.
There is a significant reason I concentrated on (1) something mechanical and (2) something from classic physics -- those two darned near cover what can be done with causality. For the biological sciences -- causality is really important but really tough. For the social sciences, they try and try, and my wife did in her Ph.D. in essentially mathematical sociology and my brother did in his Ph.D. in political science, but, net, watching my wife and brother struggle with trying to make causality work in social science, where it's so easy to make it with (1) and (2), I just said f'get about it.
You can entertain my views and my first very short post as a contribution to the discussion based on a lot of background and a claim that more is a fool's errand or just chalk it up to my ignorance.
One more point: I didn't even mention correlation. Why? Because correlation is so far from causality that it's hardly worth even mentioning.
Correlation + plausible based on your knowledge of the world implies causation (obviously to the appropriate degree). It's the flip-side of extraordinary claims require extraordinary evidence. Facebook driving Greek debt is implausible and two vaguely shaped curves aren't enough. A formula that predicts to many decimal places over a fair period, prospectively, would be really weird but hard to ignore. Spanish debt, g…
"Facebook driving Greek debt is implausible" Yes, but in reality you don't know that Plausibility is a subjective measure, and while I would say that, yes, it can be a hint, you cannot disregard something merely because it's implausible
Also, if you read my post, I'm not "disregarding" anything. I am simply saying that some things have a higher bar than others.
Earlier quoted context omitted.
I've read the book, a scattered 2 times and couldn't give you a tl;dr.
I read chapters of it a few years back, too, but forgotten the details. All in all, I was pretty convinced of the soundness of the approach at the time. My recollection is that this framework of causal inference allows one to ask questions about a probabilistic model that one can then try and measure to test causality. These questions are interventions or assertions (the do operators) that something happened. So one…
And the book's layout is fairly disjointed.
I may just begin experimenting with the theory, rather than placing it, my life is short.
This is the fundamental reason why general AI might not be possible on a computer without a body. To infer causality, you must form a hypothesis then design an experiment to confirm/deny it. Passively observing the world can't disambiguate between complex correlations or causality (even with a fancy calculus). You need action to learn the intricacies of the world. Think how the discovery of electricity led to electro…
'Body' is a rather misleading term to use for what a general AI might need.
And I say 'might' because part of the motivation for the Pearlean program is for discovering under what data and conditions one can infer causality without randomized interventions.