  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.
