Decision-Aware Safe Causal Bayesian Optimization via Structural Value of Information

Causal Bayesian optimization seeks interventions that improve the decision objective under limited information. When the causal graph is unknown, the interplay among structural uncertainty, intervention effects and execution risk further complicates intervention selection. However, reducing structural uncertainty does not necessarily benefit downstream decision-making; the value of structural information depends on its impact on intervention decisions. In this paper, we propose a decision-aware and safe sequential causal Bayesian optimization framework that explicitly evaluates the value of structural information through its impact on downstream intervention decisions. We define Structural Value of Information (SVI), which quantifies the expected improvement in downstream decision value after executing an intervention and observing its outcome, and combine it with intervention cost to form a decision-oriented acquisition criterion. To improve computational efficiency, we perform decision-aware clustering of graph hypotheses based on their induced objective and risk maps, and aggregate each cluster's posterior mass onto a representative decision map. We independently evaluate candidate interventions using the full posterior predictive mixture and filter them based on the probability of violating the joint constraints to ensure decision safety and feasibility. Through synthetic experiments and real-world case studies, we demonstrate the effectiveness of the proposed method in balancing optimization performance, structural exploration and safe execution.