3 ms·

Doesn't sound like you understand what the discussion is about.

graycat 13y ago
I responded directly to the question in the title of the post here at HN: > 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.

I responded directly to the question in the title of the post here at HN: > 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.