A Causal Understanding of When and When Not to Jeffrey ConditionalizeSkip other details (including permanent urls, DOI, citation information)
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There are cases of ineffable learning — i. e., cases where an agent learns something, but becomes certain of nothing that she can express — where it is rational to update by Jeffrey conditionalization. But there are likewise cases of ineffable learning where updating by Jeffrey conditionalization is irrational. In this paper, we first characterize a novel class of cases where it is irrational to update by Jeffrey conditionalization. Then we use the d-separation criterion (from the graphical approach to causal modeling) to develop a causal understanding of when and when not to Jeffrey conditionalize that (unlike other norms on offer) bars updating by Jeffrey conditionalization in these cases. Finally, we reflect on how the possibility of so-called “unfaithful” causal systems bears on the normative force of the causal updating norm that we advocate.