Debiased Machine Learning Solves Runtime Confounding in Counterfactual Prediction

This research introduces a vital framework for data-driven decision making that remains robust even when target populations lack full confounder measurements. By grounding the method in semiparametric efficiency theory, the authors achieve faster convergence and more reliable prediction intervals. It is a significant step forward for applying causal inference in real-world, messy data environments.