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Debiased Machine Learning of Set-Identified Linear Models

Vira Semenova

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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.

Date: 2017-12, Revised 2022-12
New Economics Papers: this item is included in nep-big and nep-ecm
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Handle: RePEc:arx:papers:1712.10024