Machine learning who to nudge: Causal vs predictive targeting in a field experiment on student financial aid renewal

In many settings, interventions may be more effective for some individuals than for 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 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 when treatment effects are difficult to estimate. We propose hybrid approaches that incorporate the strengths of predictive approaches (accurate estimation) and causal approaches (correct criterion). We show that targeting intermediate baseline outcomes is most effective in our 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%, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.