DOUBLE/DEBIASED MACHINE LEARNING FOR DYADIC DATA

doi:10.1017/S0266466625100273

DOUBLE/DEBIASED MACHINE LEARNING FOR DYADIC DATA

2026-01-01

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DOI：10.1017/S0266466625100273

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This article presents novel methods and theories for estimation and inference about parameters in statistical models using machine learning for nuisance parameter estimation when data are dyadic. We propose a dyadic cross-fitting method to remove over-fitting biases under arbitrary dyadic dependence. Together with the use of Neyman orthogonal scores, this novel cross-fitting method enables root-n consistent estimation and inference robustly against dyadic dependence. We demonstrate its versatility by applying it to high-dimensional network formation models and reexamine the determinants of free trade agreements.

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DOUBLE/DEBIASED MACHINE LEARNING FOR DYADIC DATA

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