4 Reasons Why Correlation ≠ Causation

“Statistical Secrets: Why Correlation Doesn’t Mean Causation”

(1) We’re missing an important factor (Omitted variable)

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The first reason why correlation may not equal causation is that there is some third variable (Z) that affects both X and Y at the same time, making X and Y move together. The technical term for this missing (often unobserved) variable Z is “omitted variable”.

Common Misinterpretation: One might conclude (incorrectly inferred) that buying more ice cream somehow causes an increase in drowning incidents or vice versa. In reality, the correlation is coincidental, during the summer, temperatures rise, leading to an increase in both ice cream consumption and the number of people going swimming.

(2) We got things the other way round (Reverse Causality)

The second reason why X and Y moving together may not imply that X causes Y is that Y might be causing X instead. The technical term for this is “reverse causality”.

There was an study by Nick Drydakis, an Economics professor at Anglia Ruskin University, called “The Effect of Sexual Activity on Wages” ( The Gawkar Article )