这篇论文用合成基准测了不同干预策略在供应链里的实际效果，LambdaMART 在某些场景确实更优，但数字基础设施上简单策略反而更强，值得做决策的人看看。
DACRI 研究提出 CriticalSCM-Bench v1，一个带因果真值的合成基准，用于评估供应链干预策略。相比全信息静态基准，LambdaMART 在中位归一化净值上提升 5.7% 至 16.2%，在半导体和关键材料场景有统计支持，但在数字基础设施上不如领域常量缓冲策略。部分和延迟信息下，LambdaMART 保留全信息价值的 33% 至 75%。压力测试显示干预保真度、时机、成本和未见过中断会改变策略排序，关键材料的外推保留最弱。540 次生成中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，但措辞不稳定。
DACRI: Decision-Aware Causal Intervention Ranking for Critical Supply Chains
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.