diff --git a/guide/english/machine-learning/principles/correlation-does-not-imply-causation/index.md b/guide/english/machine-learning/principles/correlation-does-not-imply-causation/index.md
index 4b4f21027c..0b90dc2dfa 100644
--- a/guide/english/machine-learning/principles/correlation-does-not-imply-causation/index.md
+++ b/guide/english/machine-learning/principles/correlation-does-not-imply-causation/index.md
@@ -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.