Why do statistical correlations not always prove that one factor causes another?

In political science and statistics, a statistical correlation indicates that two variables change together, but it does not inherently prove that one causes the other. Relying on correlation to infer causality is a significant analytical risk for several reasons:12

• Alternative Explanations: When two variables are associated, the observed changes in both may actually be the effects of an underlying third variable rather than a direct cause-and-effect relationship between the two.2
• Directionality: Correlation alone does not establish which variable is the cause and which is the effect.2
• Observational Limitations: Much research in political science and epidemiology is observational. Even in controlled settings like clinical trials, inferring true causation remains limited because studies are often subject to various alternative explanations.1

Because correlation only measures the degree of association—such as the strength of a linear relationship—conclusions regarding cause-and-effect must rely on the judgment of the analyst rather than the statistical data alone. Consequently, researchers often find that statistical findings require additional context or intuition to determine the actual nature of the relationship between variables.32