mlsanityData to 14 Oct 2026

Augmented Minimax Linear Estimation

arXiv 1712.00038 ↗First posted 30 Nov 2017stat.MEcited by 33

Not on the map. Why?

Abstract (arXiv)

Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function. This includes the average treatment effect under unconfoundedness and generalizations for continuous-valued and personalized treatments. In this paper, we discuss a general approach to estimating such quantities: we begin with a simple plug-in estimator based on an estimate of the conditional expectation function, and then correct the plug-in estimator by subtracting a minimax linear estimate of its error. We show that our method is semiparametrically efficient under weak conditions and observe promising performance on both real and simulated data.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 4; 2022: 6; 2023: 7; 2024: 4; 2025: 2; 2026: 9.

33 papers.

Sort:

By area:

  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. 2021Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approachcited by 44
  4. 2020Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparisoncited by 22
  5. 2022Future-Dependent Value-Based Off-Policy Evaluation in POMDPscited by 21
  6. 2023Augmented balancing weights as linear regressioncited by 21
  7. 2023Pseudo-Labeling for Kernel Ridge Regression under Covariate Shiftcited by 17
  8. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  9. 2022Off-policy estimation of linear functionals: Non-asymptotic theory for semi-parametric efficiencycited by 10
  10. 2024Stabilized Inverse Probability Weighting via Isotonic Calibrationcited by 9
  11. 2025Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personascited by 8
  12. 2024Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learningcited by 7
21 more
  1. 2021Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimensioncited by 6
  2. 2023Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiencycited by 6
  3. 2022Double Robust Bayesian Inference on Average Treatment Effectscited by 4
  4. 2021Robust Sample Weighting to Facilitate Individualized Treatment Rule Learning for a Target Populationcited by 3
  5. 2023Inference on Optimal Dynamic Policies via Softmax Approximationcited by 3
  6. 2026Optimally taming biases in black-box models for efficient semiparametric estimationcited by 3
  7. 2024Towards Representation Learning for Weighting Problems in Design-Based Causal Inferencecited by 2
  8. 2024Automatic debiasing of neural networks via moment-constrained learningcited by 2
  9. 2022Estimating causal effects with optimization-based methods: A review and empirical comparisoncited by 1
  10. 2023New √n-consistent, numerically stable higher-order influence function estimatorscited by 1
  11. 2026Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regressioncited by 1
  12. 2026Optimal use of a black-box learner in semiparametric estimationcited by 1
  13. 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlapcited by 0
  14. 2023A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effectscited by 0
  15. 2025Learning density ratios in causal inference using Bregman-Riesz regressioncited by 0
  16. 2026Automatic debiased machine learning and sensitivity analysis for sample selection modelscited by 0
  17. 2026Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inferencecited by 0
  18. 2026Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approachcited by 0
  19. 2026Kernel-Based Functional Balancing for Causal Inference with Compositional Treatmentscited by 0
  20. 2026Target-Aware Linear Regression Under Distribution Shiftcited by 0
  21. 2026Outcome-adapted Automatic Debiased Machine Learningcited by 0