[bsder]

Isn't the problem here that things are backward?

You don't prove causation, but you can disprove it when you find absence of correlation.

Observed correlation suggests causation which allows you to make a prediction. A prediction can be tested. The prediction will either be true or false based upon whether the correlation continues to hold.

This is one of the problems with A/B tests--they often don't have causation aka "Why?" "This dialog box was rearranged and gave us 15% better conversion." Um. Okay. But "Why?" If you can't answer "Why?" you don't have causation.

"We removed needing to enter a phone number and now have 15% better conversion." "Why?" is obvious in that case.

[led76]

I don’t think this post is trying to use correlation to prove causation. It’s in effect saying that when you can’t be sure that there is a causal relationship between two things that you can still make some decisions.

Perfect is the enemy of good as they say.

[ahmedelsama]

Yeah I love this! Well said

[asdfasgasdgasdg]

A/B tests can tell you causation. If they are correctly randomized and the tested change doesn't induce any other unintended changes, then they can tell you that the change causes whatever are the observed changes in your target metric. The challenge is controlling all other factors.

You're correct that they can't tell you the the root cause of why your change causes a particular difference, but that's a separate issue from correlation and causation.

[Kranar]

You can neither use correlation to prove causation nor can you use causation to prove correlation. X can cause Y but be uncorrelated to Y, and X can be correlated to Y without being caused by Y.

[brittanymdavis1]

You can use causation to make a decision… and you can use correlation to make a decision. Each with different implications of course - but depending on the context both can be used to do the same task.

[elcomet]

You can prove causation with an experimental study. You take a random population, split it in half and modify something for the first half, then look at the effects.