Which statement best describes the relationship between correlation and causation?

• A. Correlation indicates an association, but does not prove causation; establishing causation requires ruling out confounding and identifying a causal mechanism.
• B. Correlation proves causation.
• C. Causation does not require any association between variables.
• D. Confounding cannot affect interpretation of correlation.

Answer: (C)

The main idea here is that correlation is about association, not about proving that one thing causes another. When two variables move together, that shows there’s a relationship, but it doesn’t tell you which variable is the cause, nor does it rule out other explanations. To infer causation, you must rule out confounding—where a third variable influences both of the observed variables—and you should have a plausible mechanism showing how one variable could directly produce the other. Without addressing these, a correlation could be a coincidence or driven by another factor.

That’s why the best statement says correlation signals an association but does not prove causation, and establishing causation requires ruling out confounding and identifying a causal mechanism. The other ideas fall short: correlation does not prove causation, causation typically relies on some observable association, and confounding can indeed distort or masquerade what looks like a direct link.