Debiasing ML-or AI-Generated Regressors in Partial Linear Models

Abstract

Information systems (IS) researchers are increasingly leveraging machine learning (ML) or artificial intelligence technologies (AI) to predict feature variables from data and use them as regressors in subsequent econometric models. However, because ML/AI predictions are imperfect, these generated regressors would inevitably contain measurement errors. The direct use of such regressors in subsequent econometric models can result in biased estimation, ultimately leading to inaccurate conclusions. In light of this, we examine the problem of debiasing ML/AI-generated regressors in partial linear regression models. We propose estimators that utilize Two-Stage Least Square (TSLS) and Generalized Method of Moments (GMM) under the Double Machine Learning (DML) framework. We demonstrate the asymptotic consistency and normality of our estimators. Moreover, we conduct extensive Monte Carlo simulations and empirical applications to show the outperformance of our estimators compared with other methods. Our work advances causal inference in addressing measurement error problems arising from ML/AI-generated regressors in partial linear models and hence provides valuable practical implications for designing experimental systems and overcoming ML/AI biasedness.

Keywords: causal inference, double machine learning, AI, measurement error, partial linear