Before revision:  Once each individual arrives, the planner needs to decide instantaneously on an action or treatment assignment - for example offering job training - while taking into account various institutional constraints such as limited budget and capacity. In this paper, we show how one can use offline observational data to estimate an optimal policy rule that maximizes ex-ante expected welfare in this dynamic context.
After revision:  Once each individual arrives, the planner needs to decide instantaneously on an action or treatment assignment - for example offering job training - while taking into account various institutional constraints such as limited budget /capacity, or borrowing constraints, or the need to place people in a queue. For instance, a governmental body may receive a budget outlay at the beginning of an year, and it may need to decide how best to allocate resources within the year to individuals who arrive sequentially. In this and other examples involving inter-temporal trade-offs, previous work on devising optimal policy rules in a static context is either not applicable, or is sub-optimal. Here we show how one can use offline observational data to estimate an optimal policy rule that maximizes ex-ante expected welfare in this dynamic context.