Causal Bayes Nets and Token-Causation: Closing the Gap between Token-Level and Type-Level

Abstract

AbstractCausal Bayes nets (CBNs) provide one of the most powerful tools for modelling coarse-grained type-level causal structure. As in other fields (e.g., thermodynamics) the question arises how such coarse-grained characterizations are related to the characterization of their underlying structure (in this case: token-level causal relations). Answering this question meets what is called a “coherence-requirement” in the reduction debate. It provides details about it provides details about how different accounts of one and the same system (or kind of system) are related to each other. We argue that CBNs as tools for type-level causal inference are abstract enough to roughly fit any current token-level theory of causation as long as certain modelling assumptions are satisfied, but accounts of actual causation, i.e. accounts that attempt to infer token-causation based on CBNs, for the very same reason, face certain limitations.