3 ms·

> 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

by graycat 13y ago

> 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 chemicals in cigarette smoke that cause lung cancer. Without actually finding the chemicals, basically test the heck out of the connection, i.e., look for and reject (statistically as in an hypothesis test) other candidate causes, see if cigarette smoke does cause mutations (causing mutations are easier to test for than causing cancer, and nearly every chemical that causes cancer also causes mutations; so, if a chemical doesn't cause mutations, then it likely doesn't cause cancer; if the chemicals in cigarette smoke do cause mutations, then can't reject that they cause cancer and have to keep entertaining that the chemicals might cause cancer and have to keep testing), reject spurious correlations, do some more tests that might reject causality and observe that they do not reject, look for other causes, work hard, get tired, give up, and finally conclude that have done enough work and time to stop smoking.

new_test 13y ago
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.

by graycat 13y ago

new_test 13y ago

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.