mlsanityData to 7 Oct 2026

Towards Representation Learning for Weighting Problems in Design-Based Causal Inference

arXiv 2409.16407 ↗First posted 24 Sep 2024stat.ML cs.LG stat.MEcited by 1

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Abstract (arXiv)

Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospective cohort studies, survey weighting, and the weighting portion of augmented weighting estimators. In such applications, we explore the central role of representation learning in finding desirable weights in practice. Unlike the common approach of assuming a well-specified representation, we highlight the error due to the choice of a representation and outline a general framework for finding suitable representations that minimize this error. Building on recent work that combines balancing weights and neural networks, we propose an end-to-end estimation procedure that learns a flexible representation, while retaining promising theoretical properties. We show that this approach is competitive in a range of common causal inference tasks.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 38 of its 83 references that are in the map. First posted on arXiv 24 Sep 2024.

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38 of 83 references.

References · 38

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Learning Representations for Counterfactual Inferencecited by 327
  3. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  4. 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 156
  5. 2017Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competitioncited by 106
  6. 2019Support and Invertibility in Domain-Invariant Representationscited by 87
  7. 2020Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effectscited by 72
  8. 2018DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Trainingcited by 42
  9. 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
  10. 2016Support pointscited by 39
  11. 2016Generalized Optimal Matching Methods for Causal Inferencecited by 39
  12. 2021A Review of Generalizability and Transportabilitycited by 36
26 more
  1. 2017Augmented Minimax Linear Estimationcited by 33
  2. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  3. 2023Augmented balancing weights as linear regressioncited by 21
  4. 2021The Balancing Act in Causal Inferencecited by 20
  5. 2023Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimationcited by 20
  6. 2023Automatic Debiased Machine Learning for Covariate Shiftscited by 15
  7. 2019Permutation Weightingcited by 14
  8. 2018Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Scorecited by 12
  9. 2018Adversarial Balancing for Causal Inferencecited by 10
  10. 2023Adversarially Balanced Representation for Continuous Treatment Effect Estimationcited by 10
  11. 2021Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlapcited by 9
  12. 2022Generalization bounds and algorithms for estimating conditional average treatment effect of dosagecited by 9
  13. 2022Policy Learning with Asymmetric Counterfactual Utilitiescited by 9
  14. 2023Marginal Density Ratio for Off-Policy Evaluation in Contextual Banditscited by 9
  15. 2017Matching Using Sufficient Dimension Reduction for Causal Inferencecited by 8
  16. 2024DoubleMLDeep: Estimation of Causal Effects with Multimodal Datacited by 8
  17. 2022Neural Score Matching for High-Dimensional Causal Inferencecited by 6
  18. 2022Outcome Assumptions and Duality Theory for Balancing Weightscited by 5
  19. 2019Covariate Selection for Generalizing Experimental Results: Application to Large-Scale Development Program in Ugandacited by 4
  20. 2023Modeling Uncertain Feature Representation for Domain Generalizationcited by 3
  21. 2023Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representationcited by 3
  22. 2021Multilevel calibration weighting for survey datacited by 2
  23. 2020A Balancing Weight Framework for Estimating the Causal Effect of General Treatmentscited by 1
  24. 2022Targeted Function Balancing: Flexible balancing weights that leverage strategic imbalance to estimate causal effectscited by 1
  25. 2022Sensitivity Analysis for Survey Weightscited by 1
  26. 2022Generalized Balancing Weights via Deep Neural Networkscited by 1

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