标题： 多个处理模型下离散变化中的识别 显示英文标题

标题： Identification in Multiple Treatment Models under Discrete Variation

摘要： 我们开发了一种方法，用于在具有离散值工具变量的多处理模型中学习处理效应。我们允许进入处理的选取由一个一般的阈值交叉模型类来决定，该模型允许多维未观察到的异质性。在对未观察到的异质性的分布施加半参数限制的情况下，我们展示了如何通过一系列线性规划来计算多个处理效应参数的紧致边界，当其背后的边际处理响应函数保持非参数或被进一步参数化时。 显示英文摘要

摘要： We develop a method to learn about treatment effects in multiple treatment models with discrete-valued instruments. We allow selection into treatment to be governed by a general class of threshold crossing models that permits multidimensional unobserved heterogeneity. Under a semi-parametric restriction on the distribution of unobserved heterogeneity, we show how a sequence of linear programs can be used to compute sharp bounds for a number of treatment effect parameters when the marginal treatment response functions underlying them remain nonparametric or are additionally parameterized.