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Double LASSO: Replication and Practical Insights

Jack Fitzgerald Sice et al.

Journal of Applied Econometrics2026https://doi.org/10.1002/jae.70041article
AJG 3ABDC A*
Weight
0.50

Abstract

The rise of machine learning (ML) is one of the most prominent developments in applied econometrics in the past decade. The focus of much economic analysis is causal, rather than prediction, and Belloni et al. (2014) demonstrate how ML methods can be used in causal inference. This paper undertakes a narrow and wide replication of the Monte Carlo and empirical examples presented by Belloni et al. (2014). We discuss practical implications of this replication for the use of double ML methods in applied econometric research.

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https://doi.org/https://doi.org/10.1002/jae.70041

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@article{jack2026,
  title        = {{Double LASSO: Replication and Practical Insights}},
  author       = {Jack Fitzgerald Sice et al.},
  journal      = {Journal of Applied Econometrics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/jae.70041},
}

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Double LASSO: Replication and Practical Insights

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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

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