A causal theory of confirmation for Bayesians

This paper proposes a new Bayesian confirmation theory, according to which confirmational relations are causal relations between credences. The idea is that proposition E is evidentially relevant to proposition H relative to a credence distribution cr just in case cr(E) is a cause of cr(H), which is understood from an interventionist perspective as intervening on cr(E) would make a difference to the value of cr(H). E confirms H means that under an intervention on cr(E), cr(H) and cr(E) would covary in the same direction; disconfirmation means that they would covary in the opposite direction. I argue that this causal theory explains how orthodox Bayesian theory succeeds in non-extreme credences and fails in extreme credences, the latter known as “the old evidence problem”. Furthermore, the causal theory provides a solution to the old evidence problem that avoids serious problems faced by some other solutions. Ultimately, the causal theory of confirmation subsumes orthodox Bayesian theory as a special-case application to non-extreme credences.