The Post Double LASSO for Efficiency Analysis

2025/05/20 by Christopher F. Parmeter, Parmeter, Christopher, Artem Prokhorov +3
Computer Science · Decision Sciences · #Econometrics (econ.EM) #Efficiency Analysis Using DEA #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Machine Learning and Data Classification #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2505.14282

openalex publication_date 2025/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Big data and machine learning methods have become commonplace across economic milieus. One area that has not seen as much attention to these important topics yet is efficiency analysis. We show how the availability of big (wide) data can actually make detection of inefficiency more challenging. We then show how machine learning methods can be leveraged to adequately estimate the primitives of the frontier itself as well as inefficiency using the `post double LASSO' by deriving Neyman orthogonal moment conditions for this problem. Finally, an application is presented to illustrate key differences of the post-double LASSO compared to other approaches.

Citations

• Omitted variable bias of Lasso-based inference methods: A finite sample analysis
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