Correlation And Causation By Example

Too Long; Didn't Read

Given the observations of two features A and B: we observe <strong>correlation</strong> between features A and B, when we see a pattern where A and B change its values at the same time. When the values of A and B, increase or decrease together, we say they are positively correlated. When the value of A increases if we find that the value of B decreases proportionately and vice-versa, we say they are negatively correlated.<br>&nbsp;<br>Correlation is what we can visually identify by plotting the values of features in the graph and compare their trends for patterns. With this we cannot say what is causing what. In other words, you cannot claim one feature causing the other.<br>&nbsp;<br>When the change in one feature results in the change in the other, we call it <strong>causation</strong>. When we find correlated features, we dig around the domain, do more research and homework to increase our domain knowledge to claim that one feature is causing the other.