DACRI：面向关键供应链的决策感知因果干预排名
DACRI: Decision-Aware Causal Intervention Ranking for Critical Supply Chains
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中文总结 AI 辅助
该研究提出CriticalSCM-Bench v1基准，对比LambdaMART与恒定缓冲策略等，明确自适应干预排名在关键供应链场景的适用范围及模型复杂度的价值边界。
中文摘要 AI 辅助
检测或归因供应链中断与选择能最大化可恢复净值的干预措施并非同一回事。我们提出CriticalSCM-Bench v1，这是一个包含因果真实值、配对事实/反事实推演以及明确净值目标的受控合成基准。相较于全信息训练选择的静态基准，LambdaMART的中位数标准化净值提升了5.7%至16.2%，在半导体和关键材料原型上有配对统计支持，但在数字基础设施上无此支持。在数字基础设施领域，基于领域知识的恒定缓冲策略表现仍更优，这表明模型复杂度的提升并非总能得到合理证明。在部分和延迟场景中，LambdaMART保留了全夹紧值的33%至75%。压力测试进一步显示，干预保真度、时机、成本及保留的中断事件可改变策略排序，关键材料表现出最弱的分布外保留能力。此外，一项针对540代的受保护解释研究在确定性验证和模板回退后保留了所有固定干预决策，尽管确切措辞仍不稳定。在该受控环境中，研究结果明确了自适应排名能产生价值的场景，以及更简单的结构性策略仍更优的场景。
英文摘要
Detecting or attributing a supply-chain disruption is not the same as selecting the intervention that maximizes recoverable net value. We present CriticalSCM-Bench v1, a controlled synthetic benchmark with causal ground truth, paired factual/counterfactual rollouts, and an explicit net-value objective. Relative to a full-information train-selected static benchmark, LambdaMART improves median normalized net value by 5.7--16.2\%, with paired statistical support on the semiconductor and critical-material archetypes but not on digital infrastructure. On digital infrastructure, a domain-informed constant-buffer policy remains stronger, showing that greater model complexity is not uniformly justified. Across partial and delayed settings, LambdaMART retains 33--75\% of full-clamp value. Stress tests further show that intervention fidelity, timing, cost, and held-out disruptions can alter policy ordering. Critical materials show the weakest out-of-distribution retention. Separately, a guarded explanation study over 540 generations preserves every fixed intervention decision after deterministic validation and template fallback, although exact wording remains unstable. Within this controlled setting, the results identify regimes in which adaptive ranking adds value and those in which simpler structural policies remain preferable.
发表机构
- Independent Researcher（独立研究者）
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