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If correlation doesn’t imply causation, then what does? (2012)

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Re: If correlation doesn’t imply causation, then what does? (2012)

#51

Post by josephlord »

AlexFinks wrote: post #42
SagelyGuru wrote: post #35

It is dangerous to assume causality from any data alone. (Data and statistics are over-rated nowadays). You need to do the harder work of discovering the proper mathematical model (equation) relating explicitly the dependent (caused) variables to the independent (causing) variables. In the absence of such a verified and proven model, you just can not take a shortcut of pulling causality out of statistics, like a rabb…

But why is it dangerous, on balance, to make assumptions of causality from data and statistics alone? Animals, such as rats and ravens, face this problem all the time, and yet they can meaningfully effect the world in such manner that would imply causal understanding, and a sensitivity towards the difference between mere correlation or a correlation with causal potential. Humans do the same as well, naive people who…

> But why is it dangerous, on balance, to make assumptions of causality from data and statistics alone?

Well it all depends on the actions you take based upon those assumptions. If the action is low risk, low cost then it may be the wise choice. You need to remain aware of the uncertainty and that you are basically guessing until a better understanding is achieved.

One of the dangers is that initial uncertainty is forgotten and wrong information becomes "common knowledge".

Another danger is where there is there actually is causation but running the opposite way to that assumed. For example if a chemical substance is a useful form of self medication for sufferers of a condition there may be a correlation between the use of the chemical and the condition but banning/withdrawing/warning about the chemical would actually worsen the situation.

Re: If correlation doesn’t imply causation, then what does? (2012)

#52

Post by TillE »

cousin_it wrote: post #44

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…

> most women with the disease take a certain medicine, while most men don't

Unless "most" = nearly all, this kind of situation is not difficult to untangle if you have sufficient and appropriate data.

Re: If correlation doesn’t imply causation, then what does? (2012)

#53

Post by jtc331 »

nhebb wrote: post #14

> North: Democrat (145/154, 94 percent), Republican (138/162, 85 percent) > South: Democrat (7/94, 7 percent), Republican (0/10, 0 percent) > Overall: Democrat (152/248, 61 percent), Republican (138/172, 80 percent) This example is often cited for Simpson's Paradox, but because the South headcount for Republicans (10) and Democrats (94) had such a wide disparity and the Republican headcount was so low, it always seem…

And contrary to the OP, these results don't demonstrate a reversal of the predictors (i.e., that actually being a Democrat predicts voting for the Civil Rights Act and vice versa), but rather than we're possibly using the wrong predictors.

That is, rather than demonstrating that there is a different correlation between party and vote, it demonstrates that there is a (stronger) correlation between geography and vote.

Re: If correlation doesn’t imply causation, then what does? (2012)

#54

Post by raverbashing »

TillE wrote: post #52
cousin_it wrote: post #44

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…

> most women with the disease take a certain medicine, while most men don't Unless "most" = nearly all, this kind of situation is not difficult to untangle if you have sufficient and appropriate data.

Still, numbers are important.

What's most? 50.1%? 70%? 99.9999%?

Re: If correlation doesn’t imply causation, then what does? (2012)

#55

Post by ChikkaChiChi »

TillE wrote: post #52
cousin_it wrote: post #44

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…

> most women with the disease take a certain medicine, while most men don't Unless "most" = nearly all, this kind of situation is not difficult to untangle if you have sufficient and appropriate data.

The point is well made. Any sort of deviation from control that exhibits a pattern will directly influence the outcome of your results.

Re: If correlation doesn’t imply causation, then what does? (2012)

#56

Post by reeses »

logical42 wrote: post #7

Correlation does imply causation. It just doesn't necessarily establish it as a fact.

The problem is in distinguishing Logic from biased Rhetoric. p → q is often an inappropriate conclusion based on correlation of two vectors. The common counterexample is (r → p) ∧ (r → q).

Rhetorically, "correlation (does not imply|is not) causation" is often a red herring to dismiss a hypothesis that warrants further investigation, as correlation is essential for establishing causation.

Re: If correlation doesn’t imply causation, then what does? (2012)

#57

Post by reeses »

foobarbazqux wrote: post #26
mortehu wrote: post #22

Earlier quoted context omitted.

As described in the article, "correlation does not imply causation" would hold true even if you take "imply" to mean "is weak evidence for". This is due to Simpson's paradox[1], which says that correlation can be inverted when you take into account an additional distinguishing factor in your data. A specific example. Look at [2] and take the "y" axis to be "cigarettes per day" and the "x" axis to be "life expectancy…

Where do hypotheses come from if correlation isn't weak evidence for causation?

Actually, that is precisely why you should form a hypothesis based on correlation. A properly conducted experiment should elucidate any causative link between the elements and the direction of those elements.

Hypotheses are cheap. Create lots of them. Being 'wrong' about a hypothesis is awesome. It's the path of Reason.

(Yes, I woke up this morning wanting to capitalize terms for definitional emphasis. Sorry.)

Re: If correlation doesn’t imply causation, then what does? (2012)

#59

Post by dedward »

raverbashing wrote: post #30
joe_the_user wrote: post #27

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

Right.. but when you find correlation, you just see that two things are obviously related in some way - there is a common factor. It could be direct causation, or it could be any number of other things. The question is whether or not you have a theory that can be analyzed to see if this is a CAUSE or not... and we can't test every correlated thing out there exhaustively.

Re: If correlation doesn’t imply causation, then what does? (2012)

#60

Post by graycat »

> 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.

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