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You seem to keep arguing that your examples are not causal but only correlated. Because something does not happen 100% of the time does not mean it's not causal. Smoking causes cancer, not everyone that smokes actually gets cancer.

I'm actually not satisfied with that definition, but for now it works well enough for the sake of argument. I would like to define it as a predictor whose strength does not change when it's used transparently. But that definition is too vague for now.

And you're right that when used transparently, the predictor will probably become more accurate.

> Smoking causes sickness, but is also correlated with less obesity, and -- in the minutes/hours after smoking, is correlated with being more alert (possibly caused by a nicotine response, I don't remember the biology).

In [0]: [Since] nicotine is a metabolic stimulant and appetite suppressant, quitting or reducing smoking could lead to weight gain.

I didn't know that smoking caused less obesity. I would have thought it was because of monetary reasons.

[0] http://www.nber.org/papers/w21937




I am using this definition of causality[0], which is precise, and you use it in a much more general sense.

> I didn't know that smoking caused less obesity. I would have thought it was because of monetary reasons.

It does not cause less obesity. It is correlated with less obesity, possibly through suppressed appetite -- but it's also possible that it's actually a reflection of economic background which is also correlated with obesity.

It apparently does cause more alertness (in the same way that coffee does -- enough of it will make you awake and alert, too much will kill you).

[0] https://en.wikipedia.org/wiki/Causality#Probabilistic_causat...




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