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The paper evaluates how prediction error of nuisance functions relates to causal estimator performance, finding that prediction error is not a consistent measure of causal bias or confidence interval coverage across methods like XGBoost and Double Machine Learning.
This paper develops a staged robustness analysis framework that connects Structural Equation Modelling (SEM), Ordinary Least Squares (OLS), and Double Machine Learning (DML) for survey-based latent-construct research, demonstrated on a FinTech Digital Customer Intimacy survey model. The framework provides a reusable template for researchers to assess stability of findings across different estimation methods.