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 in our specific application, 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：在许多情境下，干预措施对某些个体可能比对其他个体更有效，因此实施针对性干预可能带来益处。我们通过一项涉及超过53,000名大学生的大规模实地实验，分析了目标定位的价值。该实验旨在运用"助推"策略，鼓励学生在非强制性截止日期前续期其助学金申请。我们首先采用基线目标定位方法：其一，基于估计异质性处理效应的因果森林模型进行目标定位，随后根据预估处理效应最高的学生分配干预措施；其二，评估两种替代性目标定位策略——分别针对未接受干预时助学金续期预测概率较低的学生与预测概率较高的学生群体。预测基线结果并非理想的目标定位标准，且应优先关注低、高或中等预测概率也缺乏先验明确性。然而，实践中基于低基线结果的目标定位仍被广泛采用，这往往是因为个体特征与处理效应之间的关系难以或无法通过历史数据进行准确估计。我们提出融合预测性方法（精确估计）与因果方法（正确标准）优势的混合策略，并证明在具体应用场景中，针对中等基线结果的目标定位最为有效，而基于低基线结果的定位反而会产生负面影响。在实验的某一年度中，对所有学生实施助推使早期申请率较37%的基线平均值平均提升6.4个百分点。我们估算，采用优选策略对半数学生进行目标定位，可获得约75%的干预效益。