Hey Clive,
Sure, whether you understand the cause is often irrelevant if you find a correlation. However, the real pitfall with this sort of datamining is that correlation doesn't necessarily imply future correlation. If you look at enough samples of random numbers, some of them are going to have an empirical correlation in your sample just by chance. This is how we get garbage like the idea that the superbowl predicts the stock market.
Heh. True! And indeed, paying too much attention to suggestive but ultimately nonsensical correlations leaves you as barking mad as that dude in the movie Pi.
But I think the point with some of the data-mining is that they find correlations that are sufficiently persistent -- i.e. they happen time and time and time again -- such that they do, in fact, become useful as for prediction. The stock-market/superbowl correlation has never achieved that level of reliability, but many other super-weird correlations have. In these latter cases, it's not that there are no logical causal connections; it's merely that they're so obscure that we can't figure out what they are. Or they might be removed by a few phases: We observe that, reliabily and with predictability, event A occurs whenever signal F is perceived. We don't know why. Forty years from now, we'll figure out that it's because F is a symptom of L that is triggered by C catalyzed by A. But merely because we don't know the link doesn't mean that we can't usefully exploit our noting the correlation between A and F.
Indeed, an astonishingly large part of everyday medicine is guided by correlation and not causation. One hundred years on, scientists still don't have a clear idea why aspirin cures headaches, but they know beyond a shadow of a doubt that it almost always does.
I like that bit about aspirin. Indeed, we don't know a whole lot about how many medicines work, we just pretend to.
I've taken the liberty of EMailng a link and a few lines from here to a couple of medical sites I read, hope that's OK.
Hey Clive,
Sure, whether you understand the cause is often irrelevant if you find a correlation. However, the real pitfall with this sort of datamining is that correlation doesn't necessarily imply future correlation. If you look at enough samples of random numbers, some of them are going to have an empirical correlation in your sample just by chance. This is how we get garbage like the idea that the superbowl predicts the stock market.
Posted by: Franco at January 10, 2003 6:53 PM
Heh. True! And indeed, paying too much attention to suggestive but ultimately nonsensical correlations leaves you as barking mad as that dude in the movie Pi.
But I think the point with some of the data-mining is that they find correlations that are sufficiently persistent -- i.e. they happen time and time and time again -- such that they do, in fact, become useful as for prediction. The stock-market/superbowl correlation has never achieved that level of reliability, but many other super-weird correlations have. In these latter cases, it's not that there are no logical causal connections; it's merely that they're so obscure that we can't figure out what they are. Or they might be removed by a few phases: We observe that, reliabily and with predictability, event A occurs whenever signal F is perceived. We don't know why. Forty years from now, we'll figure out that it's because F is a symptom of L that is triggered by C catalyzed by A. But merely because we don't know the link doesn't mean that we can't usefully exploit our noting the correlation between A and F.
Indeed, an astonishingly large part of everyday medicine is guided by correlation and not causation. One hundred years on, scientists still don't have a clear idea why aspirin cures headaches, but they know beyond a shadow of a doubt that it almost always does.
Posted by: Clive at January 12, 2003 12:22 AM
I like that bit about aspirin. Indeed, we don't know a whole lot about how many medicines work, we just pretend to.
I've taken the liberty of EMailng a link and a few lines from here to a couple of medical sites I read, hope that's OK.
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Nice weblog
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