From Risk Prediction to Risk Mechanisms: A Multi-Resolution Causal Representation for Road Safety and Motor Insurance

Abstract

Operational risk models can estimate event frequencies precisely while leaving the underlying mechanisms weakly resolved. We study this resolution mismatch using motor insurance and road safety. A revisable DAG represents trip-level crash generation; a separate predictive layer links annual rating information to latent driving states; and an observation process maps crashes into recorded liability claims. Compatibility sets collect the structural and crash-to-claim laws that reproduce an observed annual contrast under stated restrictions. External studies enter only through explicit bridge assumptions and sensitivity bounds. The framework therefore distinguishes sampling uncertainty from uncertainty about structure, observation, and study-to-target correspondence. Two limited examples illustrate the gap. A sublinear mileage relation constrains an aggregate accident rate per unit distance but not its mechanism. In French motor-liability data, the 18-20 versus 40-49 claim-frequency relativity is 3.388 in a model including vehicle and geographic variables and 1.235 when the same model also conditions on a medium-resolution bonus-malus score. These are different predictive functionals, not successive causal adjustments. A Spanish culpability estimate is used only to show how a strong cross-study bridge would restrict a toy bookkeeping region. The framework does not estimate the full DAG; it makes explicit which assumptions are needed before an annual predictive contrast can support a mechanism-specific risk statement.