Partial Identification, Distributional Preferences, and the Welfare Ranking of Policies
探讨政策选择模型中识别与偏好之间的张力，刻画了反事实政策的福利排序如何受识别假设、可行政策集和分配偏好的影响，并以STAR实验为例说明。
We discuss the tension between “what we can get” (identification) and “what we want” (parameters of interest) in models of policy choice (treatment assignment). Our nonstandard empirical object of interest is the ranking of counterfactual policies. Partial identification of treatment effects maps into a partial welfare ranking of treatment assignment policies. We characterize the identified ranking and show how the identifiability of the ranking depends on identifying assumptions, the feasible policy set, and distributional preferences. An application to the project STAR experiment illustrates this dependence. This paper connects the literatures on partial identification, robust statistics, and choice under Knightian uncertainty.