In observational research with two variables, what can be inferred about causality?

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In observational research with two variables, the main idea is that you can see whether they tend to move together, which is called an association. Causality can’t be established here because there’s no random assignment to balance other factors, so a third variable or the direction of influence could explain the link. To claim that one variable causes the other, you need a design that controls for confounding—typically randomization or other strong causal methods that can demonstrate that changes in one variable lead to changes in the other after ruling out alternative explanations. As a result, the safest and most accurate inference from this type of data is that the variables are associated.