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Comment by SagelyGuru | Hacker News Reader

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 rabbit out of a hat. Incidentally, the "Simpson's paradox" to which so much attention is given here, is a trivial illustration of the fact that you can not meaningfully add percentages from differing amounts. Something every school kid ought to learn.

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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 have never learned about experimental design, or have never learned the concept of correlation, also make useful judgments on the causal model behind ordinary problems and events.

How did these machines make actionable judgments on causality with nothing more than noisy inputs to their sensory systems? Through what technique did they discern the difference between mere correlation, and a correlation with exploitable causality?

It is probably the ability to make the abstract jump from the data and its correlations to generalised laws that marks the chief difference between people on one hand and animals and machines on the other. Correlation is only useful up to a point. It shows that there may be some relationship between the variables but it says nothing about its nature. Was X caused by Y or was Y caused by X or were X,Y both caused by some unknown Z, was it all just an accidental data sample, was it significant, with how much doubt? Does the assigned significance in fact rely on an unwarranted assumption of some underlying population distribution? Just too many questions and no answers. Besides, causality is problematic enough (see non-aristotelian philosophies and/or quantum mechanics) even without trying to demonstrate it with statistics.

> naive people who have never learned about experimental design, or have never learned the concept of correlation, also make useful judgments on the causal model behind ordinary problems and events.

They also are often... racist. Or hold whatever other stereotypes to heart. Racism is just a good example of an extreme position to hold which is often due to assumptions of causality.

"Lots of minorities are in prison. There is a high correlation between being a minority and being in prison. Therefore being a minority leads to being a criminal."

This completely ignores external reasons why minorities might end up in prison more often than others. For instance, it could be that minorities have an equal amount of criminal activity as the general population, but are more likely to end up in prison because of it. Correlation does not imply causation.

I think the number of social issues that arise due to assumptions of causation is quite high, actually, and often leads to poor decision making in policy. That is why it is "dangerous."

"Through what technique did they discern the difference between mere correlation, and a correlation with exploitable causality?"

Evolution - that is, assumptions that are accurate are favoured since they are more likely to lead to the animal surviving, vs embracing spurious correlations which are likely to get you killed.

Of course, this can break down if we try to apply the cognitive rules of thumb we've evolved to new domains outside our original evolutionary scope - our difficulties in thinking about statistics and probability are a great example of this. The math is trivial, but it just doesn't fit our brains very well without a great deal of cultural scaffolding.

> 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.

Can you expand on this idea? To me it seems fine to draw causal inferences from data alone.

It depends where the data comes from. The gold standard in science is a controlled, randomized experiment - but obviously that standard is not always attainable.

I think it helps a lot if you have a plausible explanation for the chain of causes. For example, the correlation of dead people in houses with shitty stoves and furnaces in those houses is interesting, but once you discover that said stoves and furnaces leak carbon monoxide, AND that carbon monoxide binds to hemoglobin with much greater affinity than oxygen, AND that the affected people don't receive any specific warning symptoms when that happens, you suddenly have a reason to stop wondering whether or not they were committing suicide at a heightened rate because of poverty-related depression.

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