Correlation says two variables move together. Causation says changing one actually changes the other. Correlation is symmetric and cheap to compute, while causation is directional and needs either a mechanism or an intervention behind it.
Ice cream sales and drowning deaths rise together every summer. Nobody drowns because a stranger bought a cone. Hot weather drives both, so temperature is a confounder sitting behind the correlation.
A product version bites harder. Customers who use a company's mobile app spend more than web-only customers, so a team concludes the app drives spending. More likely the heaviest customers install the app first. Reverse causation and self-selection are both live explanations. The clean way to settle it is to randomize who gets nudged to install, then compare spend between the two randomized groups.
Rewriting in plainer words…
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