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Regression adjustment in randomized controlled trials with many covariates
By employing Neyman's (1923) finite population perspective, we propose a bias-corrected regression adjustment estimator using cross-fitting, and show that the proposed estimator has favorable properties over existing alternatives.
On Using The Two-Way Cluster-Robust Standard Errors
We, therefore, hope that this paper will provide a theoretical justification for the legitimacy of most, if not all, of the thousands of those empirical papers that have used the TWCR standard errors.
Standard errors for two-way clustering with serially correlated time effects
We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels.
Dyadic double/debiased machine learning for analyzing determinants of free trade agreements
Together with the use of Neyman orthogonal scores, this novel cross fitting method enables root-$n$ consistent estimation and inference robustly against dyadic dependence.
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