Bradford Hill criteria

Hill's nine perspectives may contribute to the true causal criteria. For both experimental and epidemiological evidence, causal criteria exist of two requirements: 1. Observing a change in the suspected exposure being associated with a change in the outcome under study, and 2. Evidence supporting this associated event change, unlikely being the result of bias, accidental, confounding or any other reason.

Hill described epidemiological version of the first requirement with the words: “Is the frequency of the associated events affected? Here the strongest support for the causation hypothesis may be revealed.”

In a well set up experiment, the experimental conditions make associated event changes unlikely the result of any other reason but causality. The exclusion of non-causal reasons being behind the observations is more difficult in epidemiology. However, identifying bi-directionality of associated event changes may form strong support for the unlikelihood of bias, accidental, or confounding (or any other reason) being responsible for the associated event changes. Example: since causal relationships are characterised by associated event changes, if a population's tobacco rate exposure increase, is associated with an increase in the lung cancer rate, the associated event changes suggest the possibility of tobacco causing lung cancer. If the same or other populations decrease the tobacco rate exposure and this is associated with a decrease in lung cancer rates, the bi-directionality of the same association would be more difficult to explain using non-causal reasons. This would leave causality as the explanation best fitting the observations. Another way to provide a degree of exclusion of non-causal reasons being behind associated event changes, is identifying support for many different perspectives all fitting the causal hypothesis. Many different perspective supporting the causal hypothesis like the pieces of a single puzzle, also makes bias, accidental, and confounding reasons less likely responsible for the observed associated event changes.

Another consideration in epidemiology is the best chances of avoiding bias and accidental findings in causal inference evaluations, being the use of big data sets of raw non-selective long-term data. The big picture needs to confirm any interpretations from smaller pictures in order to validate the findings. Generalisations of any potentially reductionist interpretations require validation by zooming out.

In 1996, David Fredricks and David Relman remarked on Hill's criteria in their pivotal paper on microbial pathogenesis.

Definition

In 1965, the English statistician Sir Austin Bradford Hill outlined a set of nine principles to consider when evaluating epidemiologic evidence of a causal relationship between an exposure and outcome. (For example, he demonstrated the connection between cigarette smoking and lung cancer). The list of principles is as follows:

1. Strength (effect size): A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal.
2. Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.
3. Specificity: Causation is likely if there is a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.
4. Temporality: The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).
5. Biological gradient (dose–response 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.
6. Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).
7. Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that "lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations".
8. Experiment: "Occasionally it is possible to appeal to experimental evidence".
9. Analogy: The use of analogies or similarities between the observed association and any other associations.

Some authors consider, also, Reversibility: If the cause is deleted then the effect should disappear as well.