When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation

arXiv:2609.00071v3 Announce Type: replace-cross Abstract: Prediction error is widely used to evaluate nuisance-function estimators in causal inference, but its relationship with the performance of the resulting causal estimator is not well understood. We studied this question in a partially linear model using Monte Carlo simulations, comparing ordinary least squares (OLS), generalized additive models (GAMs), XGBoost, and Double Machine Learning with XGBoost (DML-XGBoost) with independent and with clustered observations. We evaluated nuisance-function prediction error, bias, RMSE, and 95% confidence interval coverage, and derived the second-order remainder through which errors in the exposure and outcome nuisance functions jointly determine the bias of the residualized estimator. When all methods estimated the causal effect from the same training sample, DML-XGBoost had the lowest RMSE and the highest coverage among the non-oracle methods, although XGBoost fitted to the full training sample had lower out-of-sample prediction error for both nuisance functions. GAMs had the lowest prediction error of all methods but were biased, with coverage of 0.55 with independent observations. The remainder reproduced the average bias of OLS, GAMs, and DML-XGBoost, but only half to three-quarters of the bias of XGBoost, whose residuals were formed on the data used to fit it. A joint-error measure based on the absolute cross-product of the two nuisance errors was only weakly associated with causal bias. Prediction error is useful for assessing nuisance-function estimation, but it should not be treated as a direct measure of the quality of the resulting causal estimator.