Identification in Multiple Treatment Models under Discrete Variation

• Tác giả: Vishal Kamat, Samuel Norris, Matthew Pecenco
• Ngôn ngữ: eng
• Ký hiệu phân loại: 003.1 System identification
• Thông tin xuất bản: 2023
• Bộ sưu tập: Metadata
• ID: 197663

Tóm tắt

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.