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Performing Valid Inference with AI/ML-Generated Covariates: A Guide for Empirical Practice

Author

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  • Timothy Christensen
  • Stephen Hansen

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.

Suggested Citation

  • Timothy Christensen & Stephen Hansen, 2026. "Performing Valid Inference with AI/ML-Generated Covariates: A Guide for Empirical Practice," AEA Papers and Proceedings, American Economic Association, vol. 116, pages 92-97, May.
    • Handle: RePEc:aea:apandp:v:116:y:2026:p:92-97
    DOI: 10.1257/pandp.20261020
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    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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