AI 中文总结
该研究针对机动车保险与道路安全的尺度不匹配问题，提出多尺度因果DAG框架，明确其集合性贡献，结合法西保险数据集开展分析，指出需行程级数据支撑更强机制性主张。
AI 中文摘要
道路安全机制在秒、分钟和行程尺度上运行，而机动车保险则观测的是保险年度汇总的责任索赔。因此，年度费率系数虽能准确预测索赔，却无法明确事故产生过程。本文提出一种多尺度因果DAG框架，包含三部分：一是基于72项研究边记录的结构化非详尽映射构建的事故发生图；二是连接传统费率变量与潜在风险暴露、环境及行为的独立观测层；三是包含报案、责任归属及理赔管理的下游事故到索赔过程。其正式贡献具有集合性特征：它明确了哪些年度机制规律和索赔观测映射与观测到的保险差异及保留的外部证据兼容，而非估计费率因子的因果效应。诊断示例显示了该解释的局限性：次线性里程关系在未确定其构成的情况下约束了总风险暴露。在法国freMTPL2freq保险组合中，经车辆/地理调整后，18-20岁与40-49岁群体的索赔频率相对性为3.388，在中等分辨率奖惩类别条件下为1.235；后者是不同的条件预测差异，因奖惩总结了内生的 prior保险历史。西班牙年龄中介估计在明确的交通敏感性假设下仅缩小了一个粗略记账区块，所得区间仍较宽。实践意义在于数据要求：更强的机制性主张需要行程级中间状态及关联的事故-索赔观测数据。
英文摘要
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.
