Debiased machine learning of set-identified linear models
为集合识别模型的边界（支撑函数）提供了估计和推断方法，利用Neyman正交性和样本分割构造了根号N一致、渐近正态的估计量，并提出了乘子自助法进行推断，适用于高维协变量选择场景。
This paper provides estimation and inference methods for an identified set's boundary (i.e., support function) where the selection among a very large number of covariates is based on modern regularized tools. I characterize the boundary using a semiparametric moment equation. Combining Neyman-orthogonality and sample splitting ideas, I construct a root-N consistent, uniformly asymptotically Gaussian estimator of the boundary and propose a multiplier bootstrap procedure to conduct inference. I apply this result to the partially linear model, the partially linear IV model and the average partial derivative with an interval-valued outcome.