Disease prevention is as central to healthcare as the treatment itself; this makes the ability to predict health outcomes a critical capability for healthcare systems. Established statistical methods and machine learning models provide high predictive capabilities, but inherently lack causal reasoning under the hood, limiting their use for prevention due to spurious associations, fallacies and possibilities of making paradoxical inferences. Causal inference provides a principled foundation to address those limitations, by integrating medical expertise and establish conclusions into causal models, allowing cause–effect estimations, which are needed in preventive medicine. This work attempts to combine causality and prevention in a conceptual framework, based on foundational concepts of medical prevention and causal inference. It then justifies their integration by identifying common ground between the two. Lastly, we give some examples of calculations for the causal factor contribution of risk-factors.