In 1965, Austin Bradford Hill identified nine “viewpoints” from which an association may be viewed in seeking to decide whether it is causal (Hill 1965). Hill’s nine viewpoints, often wrongly called “criteria”, have been repeated and rehashed very often indeed, with both approval and disapproval. Despite the profusion of developments in technical and non-technical literatures on causal inference since then, Hill’s viewpoints remain a starting point for discussions of causal inference in epidemiology.
There are good reasons for this. Technical advances do not eliminate the need for human judgement, and Hill’s nine viewpoints provide some structure for arriving at these judgements. And it is fair to say that the non-technical literature has not substantially advanced, at least in what it offers for practical purposes. There are other similar lists of guidelines, but it is hard to identify any clear advance, in the non-technical sphere, beyond the basic idea of identifying a few things to bear in mind when trying to decide if an association is causal. For example, Jon Williamson and Federica Russo suggest that, in the health sciences, evidence for causality must come from both population-level studies and experimental studies of underlying mechanisms (Russo and Williamson 2007). This claim may be theoretically interesting (for criticism see Broadbent 2011) but it is clearly intended as a theoretical analysis, and adds little from a practical perspective. Both items in question are covered by Hill’s list; the difference is that Hill does not think any item on his list is necessary, and the claim that evidence concerning underlying mechanisms, in particular, is necessary for a causal inference is highly doubtful in an epidemiological context, and identified as such by Hill. But however that difference is settled, as long as the debate is about what kind of evidence is or is not desired or required for a causal inference, we are not offering anything substantially more useful than what Hill has already offered.
Where should we start, if we wish to move beyond Hill-style lists? Lists of guidelines like Hill’s suffer from notable defects, despite their usefulness. They are open to misinterpretation as criteria, or as primitive algorithms for causal inference. They are a magnet for fruitless debate about exactly what should make the list, what should not, what order they should appear in, what weights to give the various components, and so forth. But most importantly – and this, I think, should be our starting point – they do not provide any clear bar that evidence must clear. The crucial question that making a decision imposes is: is the evidence good enough to act on?
A list of guidelines such as Hill’s has some heuristic value, but it does not tell us, in even the broadest terms, what constitutes enough of each item on the list. The guidelines tell us what the bar is made of, but they do not tell us how high it is. One way we might advance beyond Hill’s viewpoints, then, is to ask how good evidence needs to be before it warrants a causal inference, where that causal inference is important for a practical decision.
Broadbent, A. 2011. ‘Causal Inference in Epidemiology: Mechanisms, Black Boxes, and Contrasts’. In Causality in the Sciences, ed. Phyllis McKay Illari, Federica Russo, and Jon Williamson, 45–69. Oxford: Oxford University Press.
Hill, AB. 1965. ‘The Environment and Disease: Association or Causation?’ Proceedings of the Royal Society of Medicine 58: 259–300.
Russo, F and Williamson, J. 2007. ‘Interpreting Causality in the Health Sciences’. International Journal of the Philosophy of Science 21 (2): 157–170.