Which statement best describes causality in epidemiology?

• A. It is established by perfect correlation between IV and DV.
• B. It only requires a dose-response relationship.
• C. It involves a cause-effect relationship where changes in the independent variable are associated with changes in the dependent variable.
• D. It is established solely by randomized trials.

Answer: (C)

In epidemiology, causality means a cause-effect link where actually changing the exposure leads to a change in the outcome. The statement that best describes this says that there is a cause-and-effect relationship and that altering the independent variable is associated with, and often leads to, a change in the dependent variable. This captures the idea that the exposure can influence what happens, not just that the two stay simultaneously linked.

A key point is that causality is not about perfect correlation. You can have a strong causal relationship without a perfect one, and correlation alone can be affected by confounding or bias. A dose-response relationship can support causality, but it isn’t required in every case and by itself doesn’t prove causation. And while randomized trials provide strong evidence, they aren’t the only way to establish causality—causal inferences can come from a body of evidence across study designs, including well-conducted observational research, consistent findings, and plausibly explained mechanisms.