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Added correlation =/= causation (#22433)

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with explanatory applications.
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Dalton Harmsen
2018-11-22 05:00:14 +01:00
committed by Tom
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guide/english/machine-learning/principles/correlation-does-not-imply-causation/index.md
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@ -19,5 +19,5 @@ This is because a correlation between two variables can be explained by many rea
- There is another variable that is influencing both variables under examination. This would _not_ be a causal relationship. For example, number of cars owned and size of the house may be correlated, but these two variables are influenced by another variable: salary. An increase in the number of cars owned does not influence the size of the house.
- Correlation could be a random accident. This would _not_ be a causal relationship. This is the explanation for the previous example of margarine consumption and the divorce rate in Maine.
In machine learning, correlation suffices for making a predictive model. However, just because two variables are correlated does not mean one variable influences the other. In other words, although machine learning may help find a relationship between two variables, it does not necessarily help find the reason for the relationship.
In machine learning, correlation suffices for making a predictive model. However, just because two variables are correlated does not mean one variable influences the other. In other words, although machine learning may help find a relationship between two variables, it does not necessarily help find the reason for the relationship. Because of this, explanatory applications not only need correlation, but also causation.