We consider inference on optimal treatment assignments. Our methods are the ﬁrst to allow for inference on the treatment assignment rule that would be optimal given knowledge of the population treatment eﬀect in a general setting. The procedure uses multiple hypothesis testing methods to determine a subset of the population for which assignment to treatment can be determined to be optimal after conditioning on all available information, with a prespeciﬁed level of conﬁdence. A monte carlo study conﬁrms that the procedure has good small sample behavior. We apply the method to the Mexican conditional cash transfer program Progresa. We demonstrate how the method can be used to design eﬀicient welfare programs by selecting the right beneﬁciaries and statistically quantifying how strong the evidence is in favor of treating these selected individuals.
Armstrong, Timothy B. and Shen, Shu, "Inference on Optimal Treatment Assignments" (2013). Cowles Foundation Discussion Papers. 2320.