Yihong Gu
3 indexed papers
Publications per year
Top categories
Frequent co-authors
Research Timeline
The paper proposes a new, optimal estimator for semiparametric inference that improves upon standard double machine learning (DML) rates by eliminating the first-order stochastic error of nuisance function estimation.
This paper introduces ATLAS, a method for disentangling invariant and heterogeneous factors in multi-environment factor models, enabling transferable prediction and robust invariant-factor-only prediction.
This paper proposes a novel estimator for the target linear coefficient in a partial linear model with black-box nuisance estimation and establishes its unimprovable error rate.
Papers
Optimal use of a black-box learner in semiparametric estimation
This paper proposes a novel estimator for the target linear coefficient in a partial linear model with black-box nuisance estimation and establishes its unimprovable error rate.