Modern decision systems increasingly rely on learned components whose outputs may be confident yet wrong, exposing downstream actions to costly errors. We introduce Risk-Aware Causal Gating (RACG), a framework that decides whether to act on, defer, or abstain from a model's prediction by combining causal effect estimation with calibrated risk control. RACG models the causal pathway from candidate actions to outcomes and gates each decision according to an estimated counterfactual risk rather than raw predictive confidence. To make gating reliable, we derive distribution-free bounds on the probability of acting under high-risk conditions and show how these bounds translate into operating thresholds that satisfy user-specified safety constraints. We further propose an adaptive gating policy that adjusts to distribution shift by monitoring discrepancies between predicted and realized outcomes, tightening the gate when causal assumptions appear violated. Across simulated interventions and real-world decision benchmarks, RACG reduces high-cost errors substantially while preserving most of the utility of an ungated policy, and it outperforms confidence-based and selective-prediction baselines at matched abstention rates. Our results indicate that explicitly separating causal risk from predictive uncertainty yields decision systems that are both safer and more transparent, offering a principled mechanism for trustworthy automation in high-stakes settings.

翻译：现代决策系统日益依赖学习组件，其输出可能自信却错误，导致下游行动面临高昂代价。我们提出风险感知因果门控（RACG）框架，通过融合因果效应估计与校准风险控制，决定是否采纳、延迟或拒绝模型的预测。RACG对候选行动到结果的因果路径进行建模，并基于估算的反事实风险（而非原始预测置信度）对每个决策进行门控。为确保门控的可靠性，我们推导出高条件下行动概率的无分布界，并展示这些边界如何转化为满足用户指定安全约束的操作阈值。我们还进一步提出自适应门控策略，通过监控预测结果与实际结果之间的差异来应对分布偏移——当因果假设可能被违反时收紧门控。在模拟干预和真实决策基准测试中，RACG在保留未门控策略大部分效用的同时大幅减少高成本错误，并在相同弃权率下优于基于置信度和选择性预测的基线方法。结果表明，明确分离因果风险与预测不确定性可构建更安全、更透明的决策系统，为高风险场景下的可信自动化提供原则性机制。