提出两种新指标，帮决策者在不确定中选出最优行动。
Potential Outcome Rankings for Counterfactual Decision Making
- 引入潜在结果排名概率（PoR）和最佳结果达成概率（PoB）
- 给出指标的可识别性定理与估计方法，实证验证效果
- 适合做因果推断、个性化决策的研究者使用
面对不确定性时，反事实决策需借助因果推理从多个备选方案中选出最优动作。决策者常通过比较各行动对应的潜在结果（及其效用与吸引力）进行偏好排序。本文提出两种新度量：潜在结果排名概率（PoR）与实现最佳潜在结果的概率（PoB）。PoR 揭示个体最可能的结果排序，PoB 表示哪个行动最有可能带来最高排名结果。我们建立了这些度量的可识别性定理并推导其边界，提出估计方法。最后通过数值实验展示估计器的有限样本性质，并在真实数据集上演示应用效果。
原文摘要 · Abstract (English)
Counterfactual decision-making in the face of uncertainty involves selecting the optimal action from several alternatives using causal reasoning. Decision-makers often rank expected potential outcomes (or their corresponding utility and desirability) to compare the preferences of candidate actions. In this paper, we study new counterfactual decision-making rules by introducing two new metrics: the probabilities of potential outcome ranking (PoR) and the probability of achieving the best potential outcome (PoB). PoR reveals the most probable ranking of potential outcomes for an individual, and PoB indicates the action most likely to yield the top-ranked outcome for an individual. We then establish identification theorems and derive bounds for these metrics, and present estimation methods. Finally, we perform numerical experiments to illustrate the finite-sample properties of the estimators and demonstrate their application to a real-world dataset.
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