Debiased Machine Learning of Set-Identified Linear Models
Machine Learning
2022-12-14 v6 Machine Learning
Econometrics
Abstract
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.
Cite
@article{arxiv.1712.10024,
title = {Debiased Machine Learning of Set-Identified Linear Models},
author = {Vira Semenova},
journal= {arXiv preprint arXiv:1712.10024},
year = {2022}
}