what’s the difference between correlation and causation?
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Correlation = you analyze data and find that when variable X changes, variable Y does too, and the relationship between their change is mathematically sound (linear, exponential, whatever) and not random. But you have no idea whether variable Z is changing in the background and causing both X and Y to change with it. In that case, X and Y are not causally linked, they just have a common variable that controls them.
Example: the more you smoke, the longer you live. That’s correlation (I made that up). You can’t just say oh smoking is good for you. Because if you dig deeper, you realize the people that smoke are rich enough to buy cigarettes. And the more money you have, the longer you live.
Causation = you do an experiment, you keep everything the exact same, but in one case you change variable A and in the other variable B. If Variable C changes when variable A does, then changing A causes a change in C. It’s not so simple and you need many controls to make sure this is actually causal. But that’s the gist of it.
Example: you get two sets of mice. One you give them folic acid dissolved in sodium bicarbonate. The other, you give them just sodium bicarbonate. You do everything identically. You find those who got folic acid got kidney failure. Folic acid causes kidney failure. Usually causal experiments need to be very tightly controlled, and you manipulate the experiment yourself. With correlation, you usually just observe things that already happened, so you can’t control everything, because it’s in the past.
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