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From Rating Factors to Crash Mechanisms: A Multiscale Causal DAG Framework Linking Motor Insurance and Road Safety

Arthur Charpentier

arXiv 10 Aug 2026 · Statistics — Applications

arXiv:2608.09441 · PDF · Extracted main text

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.

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85
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188
in-text mentions
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Elvik, Rune (2023) Driver mileage and accident involvement: A synthesis of evidence0.9285480%
2Qiu, Lin and Nixon, Wilfrid A (2008) Effects of adverse weather on traffic crashes: Systematic review and meta-analysis0.8434475%
3Fell, James C. and Todd, Michael and Voas, Robert B (2011) A national evaluation of the nighttime and passenger restriction components of graduated driver licensing0.8434375%
4Moradi, Ali and Nazari, Seyed Saeed Hashemi and Rahmani, Khaled (2019) Sleepiness and the risk of road traffic accidents: A systematic review and meta-analysis of previous studies0.8435460%
5Dufournet, Marine and Lanoy, Emilie and Martin, Jean-Louis and Viall… (2016) Causal inference to detect selection bias in road safety epidemiology0.84333100%
6Greenland, Sander and Pearl, Judea and Robins, James M (1999) Causal diagrams for epidemiologic research0.84333100%
7Pearl, Judea (2009) Causality: Models, Reasoning, and Inference0.84333100%
8Gomes-Franco, Karoline and Rivera-Izquierdo, Mario and Martín-delosR… (2020) Explaining the association between driver's age and the risk of causing a road crash through mediation analysis0.81142100%
9Elvik, Rune and Vadeby, Anna and Hels, Tove and van Schagen, Ingrid (2019) Updated estimates of the relationship between speed and road safety at the aggregate and individual levels0.7373367%
10Goodwin, Arthur H. and Foss, Robert D. and O'Brien, Natalie P (2012) The Effect of Passengers on Teen Driver Behavior0.7373367%

Showing the top 10 of 85 scored citations.