In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college students, where the goal was to use "nudges" to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that estimates heterogeneous treatment effects and then assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data. We propose hybrid approaches that incorporate the strengths of both predictive approaches (accurate estimation) and causal approaches (correct criterion); we show that targeting intermediate baseline outcomes is most effective, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37% filing, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.

翻译：在许多场景中，干预措施对某些个体的效果可能优于其他个体，因此针对性地实施干预可能具有优势。本文基于一项涵盖53,000余名大学生的规模化实地实验，分析目标定位策略的价值——该实验旨在通过"助推"方式鼓励学生在非强制性截止日期前续签助学金申请。我们首先采用基线方法进行目标定位：其一，基于异质性处理效应估计的因果森林模型，将学生分配给预估处理效应最高的群体；其二，评估两种替代性定位策略，分别针对无干预状态下助学金续签概率较低和较高的学生群体。预测性基线结果并非理想的目标定位标准，且优先选择低、高或中间概率在理论上并不明确。然而，实践中普遍采用低基线结果定位策略（例如因个体特征与处理效应之间的关联常难以或无法利用历史数据估算）。我们提出融合预测方法（精准估计）与因果方法（正确准则）优势的混合策略，并证实定位中间基线结果最有效，而基于低基线结果的目标定位会产生负面效果。在实验某一年度，面向全体学生实施助推使提前办理率较37%的基线平均值提升6.4个百分点，而采用优选策略定位半数学生即可获得该效益的约75%。