Sir Austin Bradford Hill’s classic article on the characteristics of a causal relationship is well worth a read, and is still one of the most concise lists of what to look for in any research you read. Here’s a summary of what helps us make the leap from association to causation:
- Strength (is the risk is large)
- Consistency (the results have been replicated, by different researchers in different situations)
- Specificity (the predictor is not related to a broad array of outcomes)
- Temporality (predictor always precedes outcome)
- Biological gradient (also known as a dose-response: the more predictor involved, the more the outcome is involved)
- Plausibility (there is a plausible mechanism — we have a credible theory of how the causal relationship might work)
- Coherence (the association is consistent with the history of the disease)
- Experimental evidence (experimental interventions show results consistent with the association)
- Analogy (there are similar results that we can draw a relationship to)
It’s worth noting that, as Fung points out in Numbers Rule Your World, there’s an awful lot of situations where we don’t need causality. You can work with strong association in places where you only need to predict (insurance rates, at-risk determinations), and rely on causality only when you have to determine effective interventions.
The biggest problem I find with students and causality is not that they over-assign causality to situations, but that they see causality as a binary concept. In the minds of many students, there are two buckets — “caused” and “not-caused”. The idea that one association is more likely to be causal than another, that it is probably more likely that diets high in animal fat increase heart disease risk than it is that coffee cures Alzheimer’s, but that neither of these are proved beyond a doubt sort of escapes them — causality is seen as a finish line that is crossed, usually once and for all.