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.

通过使用‘鼓励’（nudges）来推动大学生在截止日期前更新财政援助申请，当研究者通过异质性治疗效应估计再将学生指派给相应的治疗组时，下一步通过衡量低预测概率及高预测概率的学生对于应用的回归效果，从而验证多种指标中通过靶向中间预线性结果是最有效的方法。