Problem
Research questions and friction points this paper is trying to address.
crash mechanisms
motor insurance
causal DAG
multiscale modeling
claim-frequency
From Rating Factors to Crash Mechanisms: A Multiscale Causal DAG Framework Linking Motor Insurance and Road Safety
🤖 AI Summary
This study addresses the lack of interpretability in accident generation processes arising from the scale mismatch between annual motor insurance rating factors and second-level road safety mechanisms. To bridge this gap, the authors propose the first multi-scale causal directed acyclic graph (DAG) framework that integrates actuarial insurance modeling with micro-level road safety mechanisms. The framework incorporates structured edge mapping, latent variable stratification into exposure–behavior–context layers, and conditional predictive contrast to jointly model accident mechanisms, insurance observations, and claims processes, prioritizing mechanistic compatibility over isolated causal effects. Empirical evaluation on the French freMTPL2freq dataset reduces the relative risk associated with age from 3.388 to 1.235, while analysis of a Spanish case reveals that mediation effects are only partially identifiable under strong assumptions, underscoring the critical importance of trip-level intermediate observational data.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multiscale causal DAG
crash mechanisms
insurance rating factors
set-valued inference
exposure decomposition
🔎 Similar Papers
No similar papers found.
