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From Risk Prediction to Risk Mechanisms: A Multi-Resolution Causal Representation for Road Safety and Motor Insurance — arXiv:2608.09441

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.09441
License (as archived): CC0-1.0
Archived: 2026-10-19T09:03:33.234Z
SHA-256 of the archived text: eb50fc870a691ec398afe4986c8b5feb45d454ec1392b64dc033ab09fe164992
Stable URL of this page: https://metraipolis.com/documento/dc_347ef6cb033a

How to cite

From Risk Prediction to Risk Mechanisms: A Multi-Resolution Causal Representation for Road Safety and Motor Insurance — arXiv:2608.09441 — arXiv (en), https://arxiv.org/abs/2608.09441 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_347ef6cb033a (Metraipolis archive dc_347ef6cb033a, sha256 eb50fc870a691ec398afe4986c8b5feb45d454ec1392b64dc033ab09fe164992)

BibTeX

@misc{dc347ef6cb033a,
  title = {From Risk Prediction to Risk Mechanisms: A Multi-Resolution Causal Representation for Road Safety and Motor Insurance — arXiv:2608.09441},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.09441},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_347ef6cb033a; sha256 eb50fc870a691ec398afe4986c8b5feb45d454ec1392b64dc033ab09fe164992},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.09441. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-11 in stat.ME).
Categorie arXiv: Applications (stat.AP) ; Econometrics (econ.EM); Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2608.09441 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:02:02.390Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).