Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional

arXiv:2212.14857 · math.ST, stat.ME, stat.ML, stat.TH · Submitted 2022-12-30 · Read on arXiv

math.ST, stat.ME, stat.ML, stat.TH

Submitted: 2022-12-30

Updated: 2026-09-05

Comments: To appear in The Annals of Statistics

Code: https://github.com/stmcg/nuisance-tuning

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Causal Inference — 9× in this paper · explained in 4 episodes
• Double Machine Learning — 2× in this paper
• Minimax Optimal Rates — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Influence Functions — 1× in this paper · explained in 2 episodes
• Minimax optimality — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Cross-Fitting and Fast Remainder Rates for Semiparametric Estimation
• Three-way Cross-Fitting and Pseudo-Outcome Regression for Estimation of Conditional Effects and other Linear Functionals
• The Fundamental Limits of Structure-Agnostic Functional Estimation
• Augmented balancing weights as linear regression
• Marginal Interventional Effects
• Semiparametric Efficient Empirical Higher Order Influence Function Estimators
• Double Cross-fit Doubly Robust Estimators: Beyond Series Regression

Related papers

• Conformal Prediction for Dyadic Regression Under Complex Missingness
• Bentkus-type asymptotic e-values
• High-Dimensional Asymptotics of Differentially Private PCA
• KL Convergence Guarantees for Score diffusion models under minimal data assumptions
• Geometric bias in eigenspace perturbation under random heterogeneous noise
• On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models