We introduce two families of stochastic interventions with discrete treatments that connect causal modeling to cost-sensitive decision making. The interventions arise from a cost-penalized information projection of the independent product of the organic propensity scores and a reference policy, yielding closed-form Boltzmann-Gibbs couplings. The induced marginals define modified stochastic policies that interpolate smoothly, via a tilt parameter, from the organic law or from the reference law toward a product-of-experts limit when all destination costs are strictly positive. The first family recovers and extends incremental propensity score interventions, retaining identification without global positivity. For inference on the expected outcomes after these policies, we derive the efficient influence functions under a nonparametric model and construct one-step estimators. In simulations, the proposed estimators improve stability and robustness to nuisance misspecification relative to plug-in baselines. The framework can operationalize graded scientific hypotheses under realistic constraints. Because inputs are modular, analysts can sweep feasible policy spaces, prototype candidates, and align interventions with budgets and logistics before committing experimental resources.

翻译：本文针对离散处理情形引入两类随机干预族，将因果建模与成本敏感决策相连接。这些干预源于有机倾向得分与参考策略的独立乘积在成本惩罚信息投影下的闭式解，由此得到玻尔兹曼-吉布斯耦合形式。通过倾斜参数的连续调节，所诱导的边缘分布定义了修正的随机策略：当所有目标成本严格为正时，该策略可从有机分布或参考分布平滑插值至专家乘积极限。第一类干预族推广了经典增量倾向得分干预方法，在无需全局正性的条件下保持可识别性。为推断这些策略实施后的期望结果，我们推导了非参数模型下的有效影响函数并构建一步估计量。仿真实验表明，相较于插件基线方法，所提估计量在稳定性与对干扰项误设的鲁棒性方面均有提升。该框架可在现实约束下实现分级科学假设的可操作化。由于输入模块化，分析者可在投入实验资源前系统扫描可行策略空间、原型候选方案，并使干预措施与预算及后勤条件相匹配。