Correlation tells us two variables move together. Causation tells us changing one variable produces a change in another. Ice cream sales and drowning deaths are correlated — both rise in summer. Banning ice cream would not prevent drownings; heat is a common cause.

In business and policy, confusing the two is expensive. A marketing team might credit a campaign for higher revenue when the economy improved for everyone. A hospital might praise a protocol that was given mainly to healthier patients.

When correlation is enough

Correlation is useful for forecasting and pattern detection. If your only goal is to predict next month's churn, a predictive model may suffice.

When your goal is to act — roll out a treatment, change a price, approve a drug, fund a program — you need a causal estimate of what the action will do, not just what tends to co-occur with success.

Moving from association to cause

Randomized experiments, when feasible, are the gold standard.
In observational data, adjust for confounders, use quasi-experimental designs, or exploit natural experiments.
Always ask: What other explanations could produce the same pattern?