  Decision makers often want to target interventions so as to maximize an
outcome that is observed only in the long-term. This typically requires
delaying decisions until the outcome is observed or relying on simple
short-term proxies for the long-term outcome. Here we build on the statistical
surrogacy and policy learning literatures to impute the missing long-term
outcomes and then approximate the optimal targeting policy on the imputed
outcomes via a doubly-robust approach. We first show that conditions for the
validity of average treatment effect estimation with imputed outcomes are also
sufficient for valid policy evaluation and optimization; furthermore, these
conditions can be somewhat relaxed for policy optimization. We apply our
approach in two large-scale proactive churn management experiments at The
Boston Globe by targeting optimal discounts to its digital subscribers with the
aim of maximizing long-term revenue. Using the first experiment, we evaluate
this approach empirically by comparing the policy learned using imputed
outcomes with a policy learned on the ground-truth, long-term outcomes. The
performance of these two policies is statistically indistinguishable, and we
rule out large losses from relying on surrogates. Our approach also outperforms
a policy learned on short-term proxies for the long-term outcome. In a second
field experiment, we implement the optimal targeting policy with additional
randomized exploration, which allows us to update the optimal policy for future
subscribers. Over three years, our approach had a net-positive revenue impact
in the range of $4-5 million compared to the status quo.
