Abstract
There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness
of interventions. One view holds that inferences about the effectiveness of interventions should be based only on
data from population-level studies (often statistical evidence from randomised trials). The other view holds that
such inferences must be based in part on mechanistic evidence. The competing views are local principles of
inference, the plausibility of which can be assessed by a more general normative principle of inference. Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical
evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how
d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is
vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits
and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends
support to those who argue that we should base our causal inferences about interventions in part on mechanistic
evidence.