In economics, it is of great importance to know what is correlation and what is causality. Also the great difference between them, given that they are two words of statistical language that are widely used in the news today.
The lack of knowledge or confusion between correlation and causality can lead to a misunderstanding of what they are telling us. Even the media can use these terms with the intention of misleading us. We must remember this phrase, since later it will make sense: correlation does not imply causality.
Conceptual difference between correlation and causality
We are going to introduce the terms, explain them and differentiate them through two examples:
- Causality: According to the RAE it means: "Cause, origin, beginning". It is a word that is used to establish a relationship between a cause and an effect. That is, it refers to the motives that originate "something." For example, if you touch fire, it causes a burn.
There is a causal relationship, since it is something that happens unequivocally and that is proven, touching fire always burns you.
- Correlation: According to the RAE it means: "Correspondence or reciprocal relationship between two or more things or series of things." In this case, the relationship that is established is one of simple correspondence or similarity, not of origin. For example, there is a correlation between the number of churches in a city and the number of alcoholics in it.
You may have even been shocked to read the previous sentence, it is true! Even if you don’t think wrong, I have said that there is a correlation, but at no time have I said that one thing causes the other. In this case there would be behind a third variable not considered in my sentence that is correlated with the two and that would be the explanatory variable. I am talking, of course, of the amount of population in that city, more population more churches and more population more alcoholics. See linear correlation coefficient
Therefore we have seen that they move in the same direction and therefore there is a correlation between the two things, but the fact that there are more churches does not imply that there are more alcoholics.
Through this last example we have been able to clearly see the difference between the two terms and that correlation does not imply causality.
There may be correlation and chance
There may also be a correlation by chance. This is, by pure coincidence. As can be seen in the graph shown. The graph compares sales in millions of dollars of organic food with the number of people diagnosed with autism. The two increase in tandem, then there is a correlation, but there is no cause that unites them.
The theoretical and practical lesson of this difference teaches us to be careful when learning to interpret the data. Not as long as there is a correlation, it means that one variable causes the other. Thus, it is important to understand the difference between correlation and causality very well. This will help us not to make mistakes when conducting studies or research.