Hill’s Criteria for Causation
A complementary approach to the aforementioned questions is a set of considerations proposed by Hill.6 The criteria evaluate the likelihood that a cause-and-effect relationship exists between
2 variables and is helpful when there are few experimental studies to use as evidence. Hill6 proposed this systematic approach for making inferences of causation from statistical associations observed in epidemiological data. He outlined 9 criteria, or in his words, viewpoints, to consider when judging whether an observed association is a causal relationship. He did not intend for his viewpoints to be used as a formal checklist and he did not believe any concrete rules of evidence could be adhered to. He emphasized that his 9 viewpoints were neither necessary nor always sufficient for causation. They provide a useful framework for systematically examining observed associations for causation.
1. Strength of Association: The magnitude of the correlation between the variables impacts the likelihood of causation. A small association does not preclude a causal effect; however, the larger the association, the more likely that it is causal. High relative risk and odds ratio are examples of strong associations between risk factors and disease occurrence that border on cause and effect.
2. Consistency: Consistent findings observed by different researchers using different settings, populations, and time frames strengthens the likelihood of an effect. Strong associations should be reproduced over and over to ensure consistency.
3. Specificity: Causation is likely if a specific causal factor produces a specific effect/disease/ injury at a specific site with no other likely explanation. This can be difficult to establish because a single risk factor may be associated with multiple outcomes. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship. The effect of cigarette smoking on mortality is a commonly cited example.
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4. Temporal Sequence: The potential cause has to precede the effect. If there is a delay between the cause and expected effect, the effect must occur after that delay. If there is a cause-and-effect relationship between general knee laxity and ACL injury, the laxity must exist prior to the ACL injury.
5. Biological Gradient (Dose-Gradient Relationship): Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed. Greater exposure leads to lower incidence. If the causative factor is removed, the effect will disappear or be reduced. An example is a causative relationship between use of an orthotic insole and the presence of foot pain. If there is causation, consistent use of the insole would result in consistent reduction in pain. If the insole is removed, the pain returns.
6. Plausibility: There needs to be a theoretical basis for proposing a cause-and-effect association between 2 variables. It must make physiological sense. For example, a relationship may exist between the price of bananas and the prevalence of low back pain, but there is not likely to be any logical connection between the 2 events. Conversely, the discovery of a correlation between social media use and the incidence of eating disorders has a foundation in societal and peer pressure being a trigger for those behaviors. Research that conflicts with established theory is not necessarily faulty. It may call for a reexamination of accepted principles.
7. Coherence: Agreement between epidemiological and laboratory findings increases the likelihood of an effect. For example, studies of blows to mannequin heads in biomechanics laboratories point to angled and side blows as being more injurious than frontal blows, which has been noted in injury surveillance studies.
8. Experiment: Evidence shows that the effect can be prevented or limited when exposed to appropriate experimental designs. For example, the incidence of a second heart attack is reduced if the patient completes a cardiac rehabilitation program.
9. Analogy: When one causal factor is known, other similar factors are likely to produce similar outcomes, and the standards for causation can be lower for this new factor.
A final step in the evaluation of the evidence is to organize the findings into a format that allows one to analyze and assess relevance. If one feels that he or she has collected sufficient information on which to act, he or she will need to determine the clinical bottom line, which provides guidance about how the evidence relates to the care one will provide.