mlsanityData to 7 Oct 2026

Adversarial Estimation of Riesz Representers

arXiv 2101.00009 ↗First posted 30 Dec 2020econ.EM cs.LG stat.MLcited by 34

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Abstract (arXiv)

Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as leading cases. Our estimators are highly compatible with targeted and debiased machine learning with sample splitting; our guarantees directly verify general conditions for inference that allow mis-specification. We also use our guarantees to prove inference without sample splitting, based on stability or complexity. Our estimators achieve nominal coverage in highly nonlinear simulations where some previous methods break down. They shed new light on the heterogeneous effects of matching grants.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Dec 2020.

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Citing papers per year: 2021: 6; 2022: 10; 2023: 5; 2024: 5; 2025: 5; 2026: 3.

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  1. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 92
  2. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 43
  3. 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
  4. 2022Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processescited by 26
  5. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  6. 2022Future-Dependent Value-Based Off-Policy Evaluation in POMDPscited by 20
  7. 2022Debiased Machine Learning without Sample-Splitting for Stable Estimatorscited by 13
  8. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  9. 2022DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learningcited by 10
  10. 2022Instrumental Variables in Causal Inference and Machine Learning: A Surveycited by 10
  11. 2021A Simple and General Debiased Machine Learning Theorem with Finite Sample Guaranteescited by 9
  12. 2023Source Condition Double Robust Inference on Functionals of Inverse Problemscited by 8
22 more
  1. 2021Learning Causal Models from Conditional Moment Restrictions by Importance Weightingcited by 7
  2. 2023Proximal Causal Learning of Conditional Average Treatment Effectscited by 7
  3. 2024Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learningcited by 7
  4. 2021Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimensioncited by 5
  5. 2024Semi-Supervised U-statisticscited by 5
  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. 2022Adversarial Estimatorscited by 2
  9. 2023Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimatorscited by 2
  10. 2024Automatic debiasing of neural networks via moment-constrained learningcited by 2
  11. 2024Program Evaluation with Remotely Sensed Outcomescited by 2
  12. 2022Data-Driven Influence Functions for Optimization-Based Causal Inferencecited by 1
  13. 2022Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sievescited by 1
  14. 2024A Mean-Field Analysis of Neural Stochastic Gradient Descent-Ascent for Functional Minimax Optimizationcited by 1
  15. 2025Preference Learning with Response Time: Robust Losses and Guaranteescited by 1
  16. 2026Optimal use of a black-box learner in semiparametric estimationcited by 1
  17. 2023Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inferencecited by 0
  18. 2025Machine learning the first stage in 2SLS: Practical guidance from bias decomposition and simulationcited by 0
  19. 2025CausalSent: Interpretable Sentiment Classification with RieszNetcited by 0
  20. 2025Latent Variable Modeling for Robust Causal Effect Estimationcited by 0
  21. 2025Learning density ratios in causal inference using Bregman-Riesz regressioncited by 0
  22. 2026Automatic debiased machine learning and sensitivity analysis for sample selection modelscited by 0