Researchers have introduced a novel framework, DACRI, for optimizing supply chain interventions by prioritizing decision-aware causal interventions. This approach focuses on maximizing recoverable net value in critical supply chains, rather than simply detecting disruptions. A key component of this framework is CriticalSCM-Bench v1, a synthetic benchmark that provides causal ground truth, paired factual and counterfactual rollouts, and a clear net-value objective. By using this benchmark, LambdaMART, a machine learning model, demonstrates improved performance in selecting effective interventions. The introduction of CriticalSCM-Bench v1 enables more accurate evaluation of supply chain management strategies, allowing for better decision-making1. This matters to supply chain managers and practitioners because it enables them to make more informed decisions about interventions, potentially minimizing losses and maximizing recoverable value.