Hill's Criteria explained with XKCD

 A few years ago I posted Hill's Criteria for assessing the evidence supporting a hypothesized causality relationship.  There are nine criteria and the statistician Lucy D’Agostino McGowan tweeted an explanation using xkcd comic strips.  Regrettably, the tweet is now lost.  

However, I have come across a different post from her which references the comics and I have been able to backtrace them.  Here are Hill's criteria with the associated illuminating comics as selected by Lucy D’Agostino McGowan.

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.