DML estimators for the quadratic functional and quadratic density integral are asymptotically inadmissible under SA models and dominated by empirical HOIF estimators, while DML remains minimax for expected conditional covariance.
Semiparametric efficient empirical higher order influence function estimators
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
verdicts
UNVERDICTED 5representative citing papers
Proposes a new estimator for β0 in the partial linear model that attains rate n^{-1/2} + δ^a_μ + (δ^s_μ)^2 with matching lower bound, eliminating first-order stochastic nuisance error.
Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.
Develops m-th order estimators for dose-response functions based on higher-order influence functions that attain the fastest known convergence rates under stated conditions.
Stochastic intervention optimizes treatment distributions to maximize expected potential outcomes when treatment count varies with n.
citing papers explorer
-
On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models
DML estimators for the quadratic functional and quadratic density integral are asymptotically inadmissible under SA models and dominated by empirical HOIF estimators, while DML remains minimax for expected conditional covariance.
-
Optimally taming biases in black-box models for efficient semiparametric estimation
Proposes a new estimator for β0 in the partial linear model that attains rate n^{-1/2} + δ^a_μ + (δ^s_μ)^2 with matching lower bound, eliminating first-order stochastic nuisance error.
-
Higher-Order Debiased Estimators for General Treatment Models
Develops higher-order influence function estimators for implicitly defined parameters in non-separable structural models using U-processes theory.
-
Fast convergence rates for dose-response estimation
Develops m-th order estimators for dose-response functions based on higher-order influence functions that attain the fastest known convergence rates under stated conditions.
-
Stochastic Intervention
Stochastic intervention optimizes treatment distributions to maximize expected potential outcomes when treatment count varies with n.