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

Abstract Causal 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) question arises how such characterizations are related to characterization their underlying structure (in this case: token-level relations). Answering meets what is called a “coherence-requirement” reduction debate. It provides details about it different accounts and same system (or kind system) each other. We argue that CBNs as inference abstract enough roughly fit any current theory causation long certain assumptions satisfied, but actual causation, i.e. attempt infer token-causation based on CBNs, very reason, face limitations.