Let’s take a look at this chart.
There is a perfect association between ice cream sales and shark attacks at the beach. When ice cream sales go up, shark attacks go up and when ice cream sales go down, shark attacks go down.
If we were to take this association seriously, in order to lessen shark attacks for public safety, we would cut down on the number of ice cream shops at the beach. Of course, this would not work. The hidden story is that more people go to the beach in the summer and this increases both the probaility of a shark attack and ice cream sales. In order to attribute the effect of ice cream sales on shark attacks, we need to establish that the effect is because of the ice cream sales, not just associated with ice cream sales.
Correlation is not causation! The causal effect is the effect produced by the treatment and not just associated with the treatment.
Let’s look at another example.
Red cars have a 7 percent higher risk of an accident [1].
If the color of the car is responsible for accidents because of visibility and that other traffic signs are also red, then it is a causal effect. If it is because young, fast and reckless drivers prefer red color car and then cause accidents, then it is not a causal effect. But it is important to know the causality from the perspective of policy. If it is indeed causal effect, it makes sense to pass a legislation restricting the sale of red cars or repainting existing cars. If it is not, all that effort would be worthless.