A thinking tool for whether X causes Y

Here's a thinking tool I learnt in med school which you might find useful for other domains. When thinking about whether X causes Y, consider the Bradford-Hill criteria - a set of 9 (or 10) principles for establishing a causal relationship between two variables, published by the epidemiologist Austin Bradford-Hill in 1965. 

The principles are (from Wikipedia):

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.

Many of the principles won't apply to the relationship you're considering, and the principles should be thought of as rules-of-thumb rather than ironclad necessities. Nevertheless, I think they are useful in generating ideas about how a possible causal relationship can be interrogated or defended. 

For the keen, here's a paper describing how advances in data and biology qualify the application of the criteria to molecular epidemiology. For the ultra-keen, here's a paper I found but haven't read, which tries to develop similar criteria to establish action-relevant, manipulable causation, for use by policy-makers.