What is the difference between correlation and causation? What makes causation difficult to prove?

Correlation is not Causation. Just because one measurement is associated with another, it does not mean it is caused by it.

The correlation shows an association between variables, but it does not show a cause-effect relationship.

Step-by-step explanation:

The correlation between two variables x and y implies that the value of the variable y increases or decreases as x changes. The correlation shows a relationship between two variables, but does not imply that x causes y.

For example, x is the number of books that a person reads annually and is the age of the person.

There may be a correlation between both variables, but it does not mean that x causes y. In other words it does not mean that all older people read more books per year than younger people

Then the correlation shows an association between variables, but it does not show a cause-effect relationship.

What makes causation difficult to prove is that you go beyond statistics. That is, another type of research is usually required to prove that a variable x causes a consequence y. Because the occurrence of an event usually has one or more complex causes associated.

For example, the sales of hamburgers in a restaurant and the number of cases of obesity of customers. It is true that these variables are related, but the obesity of people can have multiple causes, which are not explained by this analysis.

Causation mainly refers to determining the underlying reason or cause for some sort of a phenomenon.

While on the other hand, a correlation is (as the name suggests) a recognized relationship between two things or events. However, correlation does not imply causation.

The results are predictable in causation and certain while in correlation, the results are not visible or certain but there is a possibility that something will happen.