This paper applies recently developed methods to robust assessment of treatment outcomes and robust treatment choice based on nonexperimental data. The substantive question is whether young offenders should be assigned to residential or nonresidential treatment in order to prevent subsequent recidivism. A large data set on past offenders exists, but treatment assignment was by judges and not by experimenters, hence counterfactual outcomes are not identified unless one imposes strong assumptions.
The analysis is carried out in two steps. First, I show how to compute identified bounds on expected outcomes under various assumptions that are too weak to restore conventional identification but may be accordingly credible. The bounds are estimated, and confidence regions that take current theoretical developments into account are computed. I then ask which treatment to assign to future offenders if the identity of the best treatment will not be learned from the data. This is a decision problem under ambiguity. I characterize and compute decision rules that are asymptotically efficient under the minimax regret criterion. The substantive conclusion is that both bounds and recommended decisions vary significantly across the assumptions. The data alone do not permit conclusions or decisions that are globally robust in the sense of holding uniformly over reasonable assumptions.