基于最优传输的工具变量下政策相关处理效应的部分识别

在标准工具变量（IV）假设下，当工具变量在处理倾向上产生有限支撑时，政策相关处理效应（PRTE）通常难以进行点识别。我们表明，PRTE在广义 Roy 模型下的部分识别可以形式化为联合潜在结果与潜在抗性条件分布上的受限条件最优传输（CCOT）问题。 resulting multidimensional CCOT problem reduces analytically to separable one-dimensional OT problems with product costs, yielding sharp closed-form bounds and avoiding direct solution of the original high-dimensional CCOT problem. We also develop estimation and inference procedures for these bounds: for discrete instruments, we use a Double Machine Learning (DML) approach based on Neyman-orthogonal scores that accommodates high-dimensional covariates while achieving the parametric $\sqrt{n}$ rate and asymptotic normality; for continuous instruments, we explicitly characterize the corresponding nonparametric convergence rates. The framework accommodates covariates, discrete and continuous instruments, and extensions to general treatment settings. In simulations and a bed-net subsidy application, the resulting bounds are substantially tighter than the moment-relaxation method.