mlsanityData to 14 Oct 2026

Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions

arXiv 1604.07125 ↗First posted 25 Apr 2016stat.ME econ.EM math.STcited by 50

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Abstract (arXiv)

There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pre-treatment variables. The unconfoundedness assumption is often more plausible if a large number of pre-treatment variables are included in the analysis, but this can worsen the performance of standard approaches to treatment effect estimation. In this paper, we develop a method for de-biasing penalized regression adjustments to allow sparse regression methods like the lasso to be used for sqrt{n}-consistent inference of average treatment effects in high-dimensional linear models. Given linearity, we do not need to assume that the treatment propensities are estimable, or that the average treatment effect is a sparse contrast of the outcome model parameters. Rather, in addition standard assumptions used to make lasso regression on the outcome model consistent under 1-norm error, we only require overlap, i.e., that the propensity score be uniformly bounded away from 0 and 1. Procedurally, our method combines balancing weights with a regularized regression adjustment.

Key facts. Cited by 50 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Apr 2016.

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Citing papers per year: 2021: 11; 2022: 9; 2023: 10; 2024: 9; 2025: 6; 2026: 5.

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  1. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  2. 2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 71
  3. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  4. 2022Debiased Graph Neural Networks with Agnostic Label Selection Biascited by 25
  5. 2021Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confoundingcited by 21
  6. 2023Augmented balancing weights as linear regressioncited by 21
  7. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  8. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
  9. 2021Projected State-action Balancing Weights for Offline Reinforcement Learningcited by 16
  10. 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
  11. 2022Instrumental Variables in Causal Inference and Machine Learning: A Surveycited by 10
  12. 2021Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlapcited by 9
38 more
  1. 2025Direct Bias-Correction Term Estimation for Average Treatment Effect Estimationcited by 9
  2. 2022Prediction Sets for High-Dimensional Mixture of Experts Modelscited by 8
  3. 2023RCT Rejection Sampling for Causal Estimation Evaluationcited by 7
  4. 2022Outcome Assumptions and Duality Theory for Balancing Weightscited by 5
  5. 2022A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyondcited by 5
  6. 2021Automatic Double Machine Learning for Continuous Treatment Effectscited by 4
  7. 2021Estimating Average Treatment Effects with Support Vector Machinescited by 3
  8. 2023Inference on Optimal Dynamic Policies via Softmax Approximationcited by 3
  9. 2024Causal-StoNet: Causal Inference for High-Dimensional Complex Datacited by 3
  10. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  11. 2024Program Evaluation with Remotely Sensed Outcomescited by 3
  12. 2025Inference for Deep Neural Network Estimators in Generalized Nonparametric Modelscited by 3
  13. 2026Optimally taming biases in black-box models for efficient semiparametric estimationcited by 3
  14. 2024Causal inference through multi-stage learning and doubly robust deep neural networkscited by 2
  15. 2024Automatic debiasing of neural networks via moment-constrained learningcited by 2
  16. 2025A Two-armed Bandit Framework for A/B Testingcited by 2
  17. 2022Estimating causal effects with optimization-based methods: A review and empirical comparisoncited by 1
  18. 2022Confounder Balancing for Instrumental Variable Regression with Latent Variablecited by 1
  19. 2023High Precision Causal Model Evaluation with Conditional Randomizationcited by 1
  20. 2023Estimating treatment effects from single-arm trials via latent-variable modelingcited by 1
  21. 2024Stable Heterogeneous Treatment Effect Estimation across Out-of-Distribution Populationscited by 1
  22. 2024U-learning for Prediction Inference via Combinatory Multi-Subsampling: With Applications to LASSO and Neural Networkscited by 1
  23. 2024Causally-Aware Unsupervised Feature Selection Learningcited by 1
  24. 2026Optimal use of a black-box learner in semiparametric estimationcited by 1
  25. 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projectionscited by 0
  26. 2021An Interpretable Neural Network for Parameter Inferencecited by 0
  27. 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlapcited by 0
  28. 2023A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderationcited by 0
  29. 2023Addressing Dynamic and Sparse Qualitative Data: A Hilbert Space Embedding of Categorical Variablescited by 0
  30. 2023Uplift Modeling based on Graph Neural Network Combined with Causal Knowledgecited by 0
  31. 2023Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inferencecited by 0
  32. 2024Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effectscited by 0
  33. 2025Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shiftcited by 0
  34. 2025Incorporating External Controls for Estimating the Average Treatment Effect on the Treated with High-Dimensional Data: Retaining Double Robustness and Ensuring Double Safetycited by 0
  35. 2025Learning density ratios in causal inference using Bregman-Riesz regressioncited by 0
  36. 2026genriesz: A Python Package for Automatic Debiased Machine Learning with Generalized Riesz Regressioncited by 0
  37. 2026Supervised Learning Objectives for High-Dimensional Treatment Effectscited by 0
  38. 2026Anytime-Valid Confirmation of Covariate Balance for Prespecified Correctionscited by 0