Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
用机器学习选择助推对象：学生助学金续申请实验中的因果与预测定向AI 解读 ↓
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用机器学习选择助推对象：学生助学金续申请实验中的因果与预测定向
- 研究问题
- 助学金续申请提醒应优先面向处理效应较大的学生，还是基准申请概率较低或较高的学生，混合定向能否提高效果？
- 方法与思路
- 作者利用超过五万三千名学生的现场实验，比较因果森林与基准结果预测定向，并提出结合预测和因果信息的策略。
- 材料陈述的贡献
- 摘要称该应用中面向中等基准概率者最有效，低基准概率定向不利；优选策略覆盖一半学生可取得约四分之三的全覆盖收益。
- 阅读关注点
- 应核对异质性效应和策略价值的估计、策略训练与评价的样本分离，以及覆盖比例约束、跨年度差异和不确定性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要中的效果限于特定实验和指标，未说明完整评价程序与外部适用条件。
原文摘要
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.