mlsanityData to 14 Oct 2026

De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers

arXiv 1802.08667 ↗First posted 23 Feb 2018stat.ML econ.EM math.STcited by 34

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Abstract (arXiv)

We provide adaptive inference methods, based on ℓ1 regularization, for regular (semi-parametric) and non-regular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of non-regular functionals include average treatment effects, policy effects, and derivatives conditional on a covariate subvector fixed at a point. We construct a Neyman orthogonal equation for the target parameter that is approximately invariant to small perturbations of the nuisance parameters. To achieve this property, we include the Riesz representer for the functional as an additional nuisance parameter. Our analysis yields weak ``double sparsity robustness'': either the approximation to the regression or the approximation to the representer can be ``completely dense'' as long as the other is sufficiently ``sparse''. Our main results are non-asymptotic and imply asymptotic uniform validity over large classes of models, translating into honest confidence bands for both global and local parameters.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Feb 2018.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 7; 2024: 5; 2025: 5; 2026: 5.

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  1. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 95
  2. 2021Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiencycited by 56
  3. 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
  4. 2021Knowledge Distillation as Semiparametric Inferencecited by 29
  5. 2022Long-term Causal Inference Under Persistent Confounding via Data Combinationcited by 27
  6. 2021Long Story Short: Omitted Variable Bias in Causal Machine Learningcited by 23
  7. 2023Augmented balancing weights as linear regressioncited by 21
  8. 2023Doubly Robust Self-Trainingcited by 18
  9. 2022Debiased Machine Learning without Sample-Splitting for Stable Estimatorscited by 13
  10. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  11. 2023One-Step Estimation of Differentiable Hilbert-Valued Parameterscited by 11
  12. 2021A Simple and General Debiased Machine Learning Theorem with Finite Sample Guaranteescited by 9
22 more
  1. 2023Adaptive debiased machine learning using data-driven model selection techniquescited by 7
  2. 2021Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimensioncited by 6
  3. 2024Orthogonal Causal Calibrationcited by 6
  4. 2025Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approachcited by 5
  5. 2022Double Robust Bayesian Inference on Average Treatment Effectscited by 4
  6. 2023Inference on Optimal Dynamic Policies via Softmax Approximationcited by 3
  7. 2026Optimally taming biases in black-box models for efficient semiparametric estimationcited by 3
  8. 2023Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimatorscited by 2
  9. 2024Automatic debiasing of neural networks via moment-constrained learningcited by 2
  10. 2024Semiparametric inference for impulse response functions using double/debiased machine learningcited by 2
  11. 2025Learning High-dimensional Gaussians from Censored Datacited by 2
  12. 2021Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selectioncited by 1
  13. 2024General targeted machine learning for modern causal mediation analysiscited by 1
  14. 2025Double Debiased Machine Learning for Mediation Analysis with Continuous Treatmentscited by 1
  15. 2025Regularizing Extrapolation in Causal Inferencecited by 1
  16. 2026Debiased neural operators for estimating functionalscited by 1
  17. 2026Optimal use of a black-box learner in semiparametric estimationcited by 1
  18. 2023A Machine Learning Approach to Measuring Climate Adaptationcited by 0
  19. 2024Causal Machine Learning for Moderation Effectscited by 0
  20. 2025Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learningcited by 0
  21. 2026Automatic debiased machine learning and sensitivity analysis for sample selection modelscited by 0
  22. 2026Sharp Structure-Agnostic Minimax Risk for Partial Linear Modelscited by 0