Identifying Effects of Multivalued Treatments
研究在更一般的多值处理模型中识别处理效应，允许存在多维未观测异质性，依赖两个主要假设：处理分配是阈值交叉规则的可测函数，且有足够多的连续工具变量。
Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently, unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold‐crossing rules, and enough continuous instruments must be available. We illustrate our approach for several classes of models.