When making treatment selection decisions, it is essential to include a causal effect estimation analysis to compare potential outcomes under different treatments or controls, assisting in optimal selection. However, merely estimating individual treatment effects may not suffice for truly optimal decisions. Our study addressed this issue by incorporating additional criteria, such as the estimations' uncertainty, measured by the conditional value-at-risk, commonly used in portfolio and insurance management. For continuous outcomes observable before and after treatment, we incorporated a specific prediction condition. We prioritized treatments that could yield optimal treatment effect results and lead to post-treatment outcomes more desirable than pretreatment levels, with the latter condition being called the prediction criterion. With these considerations, we propose a comprehensive methodology for multitreatment selection. Our approach ensures satisfaction of the overlap assumption, crucial for comparing outcomes for treated and control groups, by training propensity score models as a preliminary step before employing traditional causal models. To illustrate a practical application of our methodology, we applied it to the credit card limit adjustment problem. Analyzing a fintech company's historical data, we found that relying solely on counterfactual predictions was inadequate for appropriate credit line modifications. Incorporating our proposed additional criteria significantly enhanced policy performance.

翻译：在进行治疗选择决策时，必须纳入因果效应估计分析以比较不同治疗或对照下的潜在结果，从而辅助最优选择。然而，仅估计个体处理效应可能不足以实现真正的最优决策。本研究通过引入额外准则（如以条件风险价值衡量的估计不确定性，该方法常用于投资组合与保险管理）来解决这一问题。对于治疗前后可观测的连续结果，我们引入了一项特定的预测条件：优先选择那些既能产生最优处理效应结果，又能使治疗后结果优于预处理水平的治疗方案，后者被称为预测准则。基于这些考量，我们提出了一种适用于多治疗选择的综合方法论。为确保满足重叠假设（该假设对于比较处理组与对照组结果至关重要），我们在使用传统因果模型前，首先训练倾向得分模型作为预备步骤。为展示本方法在实际问题中的应用，我们将其应用于信用卡额度调整问题。通过对一家金融科技公司的历史数据进行分析，我们发现仅依赖反事实预测不足以制定恰当的信用额度调整策略。引入我们提出的附加准则后，策略性能得到显著提升。