mlsanityData to 7 Oct 2026

Generalized Optimal Matching Methods for Causal Inference

arXiv 1612.08321 ↗First posted 26 Dec 2016stat.ML math.OC math.STcited by 39

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Abstract (arXiv)

We develop an encompassing framework for matching, covariate balancing, and doubly-robust methods for causal inference from observational data called generalized optimal matching (GOM). The framework is given by generalizing a new functional-analytical formulation of optimal matching, giving rise to the class of GOM methods, for which we provide a single unified theory to analyze tractability, consistency, and efficiency. Many commonly used existing methods are included in GOM and, using their GOM interpretation, can be extended to optimally and automatically trade off balance for variance and outperform their standard counterparts. As a subclass, GOM gives rise to kernel optimal matching (KOM), which, as supported by new theoretical and empirical results, is notable for combining many of the positive properties of other methods in one. KOM, which is solved as a linearly-constrained convex-quadratic optimization problem, inherits both the interpretability and model-free consistency of matching but can also achieve the √n-consistency of well-specified regression and the efficiency and robustness of doubly robust methods. In settings of limited overlap, KOM enables a very transparent method for interval estimation for partial identification and robust coverage. We demonstrate these benefits in examples with both synthetic and real data

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Dec 2016.

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Citing papers per year: 2020: 1; 2021: 11; 2022: 4; 2023: 12; 2024: 2; 2025: 6; 2026: 3.

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  1. 2023Large Language Models in Law: A Surveycited by 128
  2. 2020Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparisoncited by 22
  3. 2023Augmented balancing weights as linear regressioncited by 21
  4. 2023Pseudo-Labeling for Kernel Ridge Regression under Covariate Shiftcited by 17
  5. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  6. 2021Projected State-action Balancing Weights for Offline Reinforcement Learningcited by 16
  7. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  8. 2021NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatmentscited by 14
  9. 2025Black Box Causal Inference: Effect Estimation via Meta Predictioncited by 13
  10. 2022Inference on Strongly Identified Functionals of Weakly Identified Functionscited by 12
  11. 2021Optimal transport weights for causal inferencecited by 7
  12. 2022Neural Score Matching for High-Dimensional Causal Inferencecited by 6
27 more
  1. 2023Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiencycited by 6
  2. 2021Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimensioncited by 5
  3. 2021Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curvescited by 5
  4. 2022Outcome Assumptions and Duality Theory for Balancing Weightscited by 5
  5. 2021Matching for causal effects via multimarginal unbalanced optimal transportcited by 4
  6. 2021Estimating Average Treatment Effects with Support Vector Machinescited by 3
  7. 2021Robust Sample Weighting to Facilitate Individualized Treatment Rule Learning for a Target Populationcited by 3
  8. 2021Instrument Space Selection for Kernel Maximum Moment Restrictioncited by 2
  9. 2021Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selectioncited by 1
  10. 2021Case-based off-policy policy evaluation using prototype learningcited by 1
  11. 2022Estimating causal effects with optimization-based methods: A review and empirical comparisoncited by 1
  12. 2023Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metricscited by 1
  13. 2023Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomescited by 1
  14. 2024Multivariate root-n-consistent smoothing parameter free matching estimators and estimators of inverse density weighted expectationscited by 1
  15. 2024Towards Representation Learning for Weighting Problems in Design-Based Causal Inferencecited by 1
  16. 2025Federated Instrumental Variable Analysis via Federated Generalized Method of Momentscited by 1
  17. 2023De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Networkcited by 0
  18. 2023VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inferencecited by 0
  19. 2023Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?cited by 0
  20. 2023A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effectscited by 0
  21. 2025A primer on optimal transport for causal inference with observational datacited by 0
  22. 2025Forests for Differences: Robust Causal Inference Beyond Parametric DiDcited by 0
  23. 2025Structure Maintained Representation Learning Neural Network for Causal Inferencecited by 0
  24. 2025A Sensitivity Approach to Causal Inference Under Limited Overlapcited by 0
  25. 2026Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rulecited by 0
  26. 2026Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inferencecited by 0
  27. 2026Causal Risk Minimization for High-Dimensional Treatmentscited by 0