arbiter
JournalsPricingAccount

DOUBLE/DEBIASED MACHINE LEARNING FOR DYADIC DATA

Harold D. Chiang et al.

Econometric Theory2026https://doi.org/10.1017/s0266466625100273article
AJG 4ABDC A*
Weight
0.50

What the paper says

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.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1017/s0266466625100273

Or copy a formatted citation

@article{harold2026,
  title        = {{DOUBLE/DEBIASED MACHINE LEARNING FOR DYADIC DATA}},
  author       = {Harold D. Chiang et al.},
  journal      = {Econometric Theory},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/s0266466625100273},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

DOUBLE/DEBIASED MACHINE LEARNING FOR DYADIC DATA

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.