Abstract This chapter argues that objective Bayesianism is the interpretation of probability that best fits causal modelling in the social sciences. An overview of the leading interpretation is offered. The argument for objective Bayesianism is structured in three steps: first, I maintain that we need an interpretation that can account for single-case and generic probabilistic causal claims; second, I show that the empirically-based and objective Bayesian interpretations are apt to do this; third, I claim that the objective Bayesian version is preferable on the grounds that it leaves no room to arbitrariness, enlightens the design and interpretation of tests, and is a better guide for action.
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(2009). Methodological Consequences: Objective Bayesianism. In: Causality and Causal Modelling in the Social Sciences. Methodos Series, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8817-9_5
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