Road safety mechanisms operate within seconds, minutes and trips, whereas motor insurance observes liability claims aggregated over policy years. An annual rating coefficient can therefore predict claims accurately while leaving the crash-generating process unresolved. We propose a multiscale causal DAG framework with three parts: a proposed crash-occurrence graph constructed from a structured, non-exhaustive map of 72 study--edge records; a separate observation layer linking conventional rating variables to latent exposure, context and behaviour; and a downstream crash-to-claim process that includes reporting, responsibility attribution and claim administration. The formal contribution is set-valued: it characterizes which annual mechanism laws and claim-observation mappings are compatible with an observed insurance contrast and retained external evidence, rather than estimating a causal effect of a rating factor. Diagnostic examples show the limits of that interpretation. A sublinear mileage relation constrains aggregate exposure without identifying its composition. In the French freMTPL2freq portfolio, the 18--20 versus 40--49 claim-frequency relativity is 3.388 after vehicle/geographic adjustment and 1.235 after conditioning on medium-resolution bonus--malus categories; the latter is a different conditional predictive contrast because bonus--malus summarizes endogenous prior insurance history. A Spanish age-mediation estimate narrows only one coarse bookkeeping block under explicit transport-sensitivity assumptions, and the resulting region remains wide. The practical implication is a data requirement: stronger mechanistic claims need trip-level intermediate states and linked crash--claim observations.

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