Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands
This paper addresses statistical inference for smooth functionals of nonparametric M-estimators—such as causal effects, quantiles, and survival functions—by proposing the autoDML framework, which automates debiasing without manual influence function derivation. Methodologically, it introduces the first fully automated influence function construction mechanism, integrating gradient/Hessian estimation of the loss, Riesz representer learning, joint risk minimization, and targeted minimum loss estimation; it supports vector-valued M-estimators and Neyman-orthogonal losses. Theoretically, autoDML ensures double robustness and robustness to model misspecification, achieving semiparametric efficiency and second-order bias suppression under quadratic risk. Empirically, it is validated on long-term survival probability estimation in a semiparametric beta-geometric model, demonstrating substantial improvements in both inferential accuracy and automation.