Learn optimal policies via doubly robust empirical welfare
maximization over trees. Given reward estimates, the algorithm finds a
rule-based treatment allocation, where the policy takes the form of a
shallow decision tree that is globally optimal (or nearly so). Methods are
described in Sverdrup, Kanodia, Zhou, Athey, and Wager (2020)