Arguably, causation is merely a human word for "A always happens when B happens."
A causes B or vice versa is one of those things where it's true until it isn't.
Causation is frequently used to mean that B will always occur when A occurs. The problem is that no probability is truly 100%.
See the example used in the twitter thread I linked:
> Imagine driving a car, reaching a hill and pumping the gas as you begin to go up so that your speed is constant. The correlation between pressing on the gas and the speed of the car is zero but they're obviously causally related, it's that the agent is optimizing speed!
But more to the point, there isn't a good definition of causation. If you use the "but for A, B would not occur" there are problems. Other definitions also have problems.
This is a deep epistemological problem and I think not as simple as it may seem.
> Probably the shortest true statement that can be made about causality and correlation is "Empirically observed covariation is a necessary but not sufficient condition for causality."
Correlation isn't necessary for causation, so ironically the supposed mutilation used to fit on a PP slide - "Correlation is not causation" - is actually far more correct.
The same mistake is also made in Kahneman, Sibony and Sunstein's new book [1].
[1] https://twitter.com/economeager/status/1395791301627596806