mlsanityData to 7 Oct 2026

Balancing Covariates via Propensity Score Weighting

arXiv 1609.07494 ↗First posted 23 Sep 2016stat.MEcited by 65

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Abstract (arXiv)

Covariate balance is crucial for unconfounded descriptive or causal comparisons. However, lack of balance is common in observational studies. This article considers weighting strategies for balancing covariates. We define a general class of weights---the balancing weights---that balance the weighted distributions of the covariates between treatment groups. These weights incorporate the propensity score to weight each group to an analyst-selected target population. This class unifies existing weighting methods, including commonly used weights such as inverse-probability weights as special cases. General large-sample results on nonparametric estimation based on these weights are derived. We further propose a new weighting scheme, the overlap weights, in which each unit's weight is proportional to the probability of that unit being assigned to the opposite group. The overlap weights are bounded, and minimize the asymptotic variance of the weighted average treatment effect among the class of balancing weights. The overlap weights also possess a desirable small-sample exact balance property, based on which we propose a new method that achieves exact balance for means of any selected set of covariates. Two applications illustrate these methods and compare them with other approaches.

Key facts. Cited by 65 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Sep 2016.

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Citing papers per year: 2021: 10; 2022: 9; 2023: 14; 2024: 6; 2025: 17; 2026: 9.

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  2. 2021Mandoline: Model Evaluation under Distribution Shiftcited by 55
  3. 2023Diagnosing Model Performance Under Distribution Shiftcited by 37
  4. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
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  8. 2021End-to-End Balancing for Causal Continuous Treatment-Effect Estimationcited by 9
  9. 2021On the Distinction Between "Conditional Average Treatment Effects" (CATE) and "Individual Treatment Effects" (ITE) Under Ignorability Assumptionscited by 9
  10. 2022Adversarial De-confounding in Individualised Treatment Effects Estimationcited by 9
  11. 2023Covariate balancing using the integral probability metric for causal inferencecited by 9
  12. 2022Neighborhood Adaptive Estimators for Causal Inference under Network Interferencecited by 8
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  5. 2025Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimandscited by 5
  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. 2023Calibrated and Conformal Propensity Scores for Causal Effect Estimationcited by 3
  9. 2022Robust Causal Learning for the Estimation of Average Treatment Effectscited by 2
  10. 2025Federated Causal Inference from Multi-Site Observational Data via Propensity Score Aggregationcited by 2
  11. 2025A Two-armed Bandit Framework for A/B Testingcited by 2
  12. 2025Causal Judge Evaluation: Calibrated Surrogate Metrics for LLM Systemscited by 2
  13. 2021Case-based off-policy policy evaluation using prototype learningcited by 1
  14. 2022Estimating causal effects with optimization-based methods: A review and empirical comparisoncited by 1
  15. 2022Generalized Balancing Weights via Deep Neural Networkscited by 1
  16. 2023Contagion Effect Estimation Using Proximal Embeddingscited by 1
  17. 2023balance -- a Python package for balancing biased data samplescited by 1
  18. 2023Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Datacited by 1
  19. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
  20. 2024Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variablescited by 1
  21. 2025Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumptioncited by 1
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  23. 2025MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliabilitycited by 1
  24. 2025Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimationcited by 1
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  26. 2025Text Rationalization for Robust Causal Effect Estimationcited by 1
  27. 2026Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunitiescited by 1
  28. 2026Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inferencecited by 1
  29. 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projectionscited by 0
  30. 2021Robust Orthogonal Machine Learning of Treatment Effectscited by 0
  31. 2021Locally Learned Synaptic Dropout for Complete Bayesian Inferencecited by 0
  32. 2022Multiple Domain Causal Networkscited by 0
  33. 2022Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Datacited by 0
  34. 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlapcited by 0
  35. 2023Feedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-outcited by 0
  36. 2023A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effectscited by 0
  37. 2023Uplift Modeling based on Graph Neural Network Combined with Causal Knowledgecited by 0
  38. 2023Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groupscited by 0

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