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Causation implies a cause-and-effect relationship between variables; a change in one variable ‘causes’ a change in the other. When two variables have a causal relationship, a change in the independent variable (or the ‘cause’) influences a change in the dependent variable (or the ‘effect’).
There are four main criteria for causality:
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Let’s take a look at how cause-and-effect relationships are derived in fields of study such as statistics and medicine:
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The following are a few examples of causal relationships between variables:
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Correlation is a term that simply implies the existence of a relationship, or statistical association, between variables. Although causation also implies a relationship between variables, it goes farther to define the type of relationship as well. Causal relationships exist only when one variable ‘causes’ a change in the other.
It is important to note that although causation always implies correlation, correlation does not imply causation. But why?
For two variables to have a causal relationship, they must have a statistical association, and they must therefore be correlated. However, there are circumstances where correlation does not imply causation. These are a few reasons why correlation does not imply causation:
Causation implies the existence of a cause-and-effect relationship between variables; when one changes, it causes a change in the other.
Correlation simply implies the existence of a relationship between variables while causation implies the existence of a causal (cause-and-effect) relationship between the variables. Causal relationships exist when a change in one variable influences a change in the other.
Correlation does not imply causation because;
When two variables have a cause-and-effect relationship, the “cause” is known as the independent variable while the “effect” is known as the dependent variable; when there is a change in the independent variable, it causes a change in the dependent variable.
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