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.
Christensen et al. (Fri,) studied this question.