Causation can be implied when the relationship between an exposure and a disease has a certain number of characteristics as defined by Sir Austin Bradford Hill’s Guidelines for Causation. Notably, causation is inferred through “causal inference”. The nine characteristics of a cause are the strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experimental evidence, and analogy. Strength of association refers to the assertion that if an association is stronger, there is more likely causation. Consistency refers to the assertion that if an association is observed in many ways and places, there is more likely causation. Specificity refers to the assertion that only a single cause and a single disease can be causal. Temporality refers to the assertion that the disease must follow the exposure and is the only necessary characteristic of causality. Biological gradient refers to the assertion that if the strength of exposure is associated with the strength of disease, there is likely causation. Plausibility and coherence refer to the assertion that previous models should be able to explain the association and the association should not conflict with current knowledge. Experimental evidence, through RCTs, can also imply causation. Finally, analogy refers to the assertion that is an association is similar to other associations, there is more likely causation. While many of Hill’s guidelines face various modern critiques and have many exceptions, his guidelines have helped guide the process of inferring causation. Prospective cohort study designs as well as experimental study designs lend themselves well to implying causation, as temporality and experimental evidence are both strong indicators of causality. On the other hand, study designs such as the cross-sectional study design are unable to determine temporality, limiting its ability to establish causation.