Speaker: Jacob Stegenga (Cambridge)
Title: Bayesian Mechanista
Abstract: There are two radical views regarding the role of mechanisms in causal inference. One holds that causal inference, at least in medicine and the social sciences, should be based only on data from population-level studies (statistical evidence). The other holds that causal inference must be based in part on mechanistic evidence. This paper appeals to Bayesian confirmation theory to defend a middle view, and explains why the arguments for both sides can seem compelling. The competing views are local principles of inference, the plausibility of which can be assessed by a general normative principle of inference. The Bayesian 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 does x' is knowledge of how d does x. Thus, reasoning about causal relations by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference.
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Contemporary debates in philosophy of science
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