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Frequency-driven probabilities in quantitative causalanalysis

This paper addresses the problem of the interpretation of probability in quantitative causal analysis. I argue that probability has to be interpreted according to a Bayesian framework in which degrees of belief are frequency-driven. This interpretation can account for the peculiar use and meaning of probability in generic and single-case causal inferences involved in this domain.