A central problem in natural science is identifying general laws of cause and effect. Medical science is devoted to revealing causal relationships in humans. The framework for causal inference applied in epidemiology can contribute substantially to clearly specifying and testing causal hypotheses in many other areas of biomedical research. In this article, we review the importance of defining explicit research hypotheses to make valid causal inferences in medical studies. In the counterfactual model, a causal effect is defined as the contrast between an observed outcome and an outcome that would have been observed in a situation that did not actually happen. The fundamental problem of causal inference should be clear; individual causal effects are not directly observable, and we need to find general causal relationships, using population data. Under an “ideal” randomized trial, the assumption of exchangeability between the exposed and the unexposed groups is met; consequently, population-level causal effects can be estimated. In observational studies, however, there is a greater risk that the assumption of conditional exchangeability may be violated. In summary, in this article, we highlight the following points: (1) individual causal effects cannot be inferred because counterfactual outcomes cannot, by definition, be observed; (2) the distinction between concepts of association and concepts of causation and the basis for the definition of confounding; (3) the importance of elaborating specific research hypotheses in order to evaluate the assumption of conditional exchangeability between the exposed and unexposed groups; (4) the advantages of defining research hypotheses at the population level, including specification of a hypothetical intervention, consistent with the counterfactual model. In addition, we show how understanding the counterfactual model can lay the foundation for correct interpretation of epidemiologic evidence.