Correlation is a statistical measure that indicates how two or more variables move together.

• It is measured between 0 to 1.
• Positive correlation → variables move in same direction.
• Negative correlation → variables move in opposite direction.

Causation indicates that one event directly causes another event to occur. (Causing something to happen).

What’s the Difference?

The major distinction is that just because two variables are correlated does not mean that one causes the other to happen.

Here, chicken and moon are correlated but eating chicken does not ensure you will go to the moon hence this is not causation.

Example: A child has started watching online videos in his mobile the whole day. Now the internet bills has went high along with his grades going significant down.

Watching online videos has caused high internet bills and low grades. So, it's causation.

But high internet bills and low grades are negatively correlated but these events didn't cause each other's events to happen. So, it's a correlation.

When there is a common cause between two variables, then they will be correlated. This is part of the reasoning behind the less-known phrase, “There is no correlation without causation".

The two variables may not be a direct cause of each of them, but it’s there somewhere “upstream” in the picture. It turns out that if you don’t include hidden common causes in your model, you’ll estimate causal effects incorrectly.

Simple examples: I should eat, because the causal effect of “eating” on “hunger” is negative. I should show more impressions of this ad, because the causal effect of impressions on page views is positive.

Now, that you have understood what does the "Correlation does not imply causation means".

So have fun kiddo 😎