Correlation Versus Causation

Correlation means two variables move together. Causation means a change in one produces a change in the other. A strong correlation can exist without a causal link, and confusing the two leads to wrong decisions.

Why They Differ

There are three common reasons a correlation appears without causation:

• Confounding variable: a third factor drives both variables.
• Reverse direction: the assumed effect is actually the cause.
• Coincidence: with enough variables, some pairs will correlate by chance alone.

Example: Monthly ice cream sales and the number of sunburn cases are strongly correlated. Ice cream does not cause sunburn. Hot weather is the confounder because it increases both sales and time spent in the sun.

Establishing Causation

Randomized experiments are the most reliable method. Random assignment spreads confounders evenly across groups, so any difference in outcomes can be credited to the treatment.

This is why A/B tests support causal claims, while a simple correlation in observational data does not. When an experiment isn't possible, analysts use methods like controlling for known confounders, but the conclusion remains weaker.