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Correlation and causation are two related but distinct concepts. It is vital to understand the differences between them in order to effectively evaluate and interpret scientific research. Within this article, we will explore correlation and causation separately before delving into their comparisons.
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In analytics, correlation is a term that is used to indicate a statistical association between variables; when one variable changes, so does the other. However, it is important to note that this covariation doesn’t necessarily imply a direct or indirect causal link between the variables.
There are three main types of correlations between variables:
Consider the following example: When the temperature rises in the summer, ice cream sales and swimsuit sales both see an increase. However, although ice cream sales and swimsuit sales do depict a positive correlation, there isn’t a causal link between these variables. Rather, the rise in temperature separately influences both these variables resulting in their positive correlation.
In analytics, causation is a term that is used to indicate a causal relationship between two variables; one variable is dependent on the other. In a causal relationship, there is an independent variable (‘the cause’) and a dependent variable (‘the effect’).
Let’s continue with the aforementioned example:
We’ve established that ice cream sales and swimsuit sales depict a positive correlation in the summer due to the rise in temperature. In this example, temperature has a causal relationship with both ice cream and swimsuit sales separately. When the temperature rises, more people purchase ice creams; when the temperature rises, more people purchase swimsuits.
Causation always implies correlation, but correlation does not imply causation. But why?
Correlation simply implies a relationship between variables, but does not indicate that the covariation exists due to a direct or causal link between them. Conversely, causation goes beyond implying a relationship; it implies a specific type of relationship known as a causal relationship (or cause-and-effect relationship). For a causal relationship to exist between two variables, one must influence the other; when one variable changes, it must bring about a change in the other. The variables, therefore, have a causal link.
Reflecting back on the initial statement; variables that have a causal relationship are related and, therefore, causation always implies correlation. However, as variables can be related without directly influencing each other, correlation does not imply causation.
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Let’s understand a few reasons as to why we cannot imply causation even if we identify correlation:
A few reasons why correlation may not imply causation are;
Correlation can be defined as a statistical association between variables.
Causation implies that a change in one variable influences a change in the other and there is, therefore, a causal link between them.
Correlation simply implies a statistical association, or relationship, between two variables. Causation, on the other hand, not only implies a relationship, it implies a causal relationship; it implies that a change in one variable is directly causing a change in the other.