Hill's Criteria

In 1965, Sir Austin Bradford Hill  published The Environment and Disease: Association or Causation?   Hill was an English epidemiologist and statistician who pioneered the randomized clinical trials.  This paper laid out his criteria for establishing whether there was a causal relationship between an event and an outcome.  These are known as Hill's Criteria and are relevant far beyond the field of medical research.

In an era where information production is gargantuan and trusted sources few, we need reliable filters to sort the wheat from the chaff.  Hill's Criteria is one source of such filtering.

Hill's Criteria:

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.

Consistency (reproducibility): Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.

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.

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).

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.

Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).

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".

Experiment: "Occasionally it is possible to appeal to experimental evidence".

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.

An entertaining version is in this thread.

It can be very challenging to assess what information to believe. One simple way to evaluate evidence is via something called "Hill's Criteria", 9️⃣ considerations to help assess whether an observation has a *causal* component

I'll describe them using xkcd comics 🎉

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— Lucy D’Agostino McGowan (@LucyStats) April 23, 2020