AEA Papers and Proceedings
ISSN 2574-0768 (Print) | ISSN 2574-0776 (Online)
Performing Valid Inference with AI/ML-Generated Covariates: A Guide for Empirical Practice
AEA Papers and Proceedings
(pp. 92–97)
Abstract
Researchers increasingly use AI and machine learning to generate variables that are used in regression analysis. Ignoring measurement error in these variables can yield biased estimators and invalid inference. The methods that exist for bias correction require extensive validation data, which are typically not available in economic applications. We describe bias correction methods that do not require such data and show how empiricists can implement them via the Python package ValidMLInference. We illustrate with two applications: estimating the association between salary and remote work, and estimating long-run interest rate reactions to the sentiment expressed in Federal Open Market Committee statements.Citation
Christensen, Timothy, and Stephen Hansen. 2026. "Performing Valid Inference with AI/ML-Generated Covariates: A Guide for Empirical Practice." AEA Papers and Proceedings 116: 92–97. DOI: 10.1257/pandp.20261020Additional Materials
JEL Classification
- C38 Classification Methods; Cluster Analysis; Principal Components; Factor Models
- C45 Neural Networks and Related Topics
- C51 Model Construction and Estimation
- C87 Econometric Software
- E58 Central Banks and Their Policies