Double LASSO: Replication and Practical Insights

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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Publication Details

Journal
Journal of Applied Econometrics
Published
2026-02-15
DOI
https://doi.org/10.1002/jae.70041
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Double LASSO: Replication and Practical Insights

Jack Fitzgerald Sice, Tim Robinson, Finn Lattimore, Anna Zhu
Journal of Applied Econometrics
Advanced Causal Inference Techniques
article

Double LASSO: Replication and Practical Insights

Jack Fitzgerald Sice, Tim Robinson, Finn Lattimore, Anna Zhu
article en

Abstract

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.

Journal of Applied Econometrics
The University of Melbourne (AU), Reserve Bank of Australia (AU), Gradient (United States) (US), RMIT University (AU)
Decent work and economic growth
Openalex Percentile: Top 57%
Advanced Causal Inference Techniques
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