Primary Submission Category: Machine Learning and Causal Inference
Debiasing Machine-Learning- or AI-Generated Regressors in Partial Linear Models
Authors: Jingwen Zhang, Wendao Xue, Yifan Yu, Yong Tan,
Presenting Author: Jingwen Zhang*
Researchers are increasingly leveraging machine learning (ML) or artificial intelligence technologies (AI) to predict feature variables 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 both linear and partial linear regression models. We propose estimators that utilize the Two-Stage Least Square (TSLS) and the Generalized Method of Moments (GMM) under the Double Machine Learning (DML) framework. We demonstrate the asymptotic consistency and normality of our estimators and conduct extensive Monte Carlo simulations 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. Our work provides valuable practical implications for designing experimental systems and overcoming ML/AI biasedness.