DACRI: Decision-Aware Causal Intervention Ranking for Critical Supply Chains
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Computer Science > Machine Learning
Title:DACRI: Decision-Aware Causal Intervention Ranking for Critical Supply Chains
Abstract: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.
| Comments: | Accepted for presentation at the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2026), 15--17 October 2026, Bali, Indonesia. 5 tables; no figures. Benchmark and code: this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.11154 [cs.LG] |
| (or arXiv:2608.11154v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.11154
arXiv-issued DOI via DataCite (pending registration)
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