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DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training

arXiv 1802.05664 ↗First posted 15 Feb 2018stat.MLcited by 42

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Abstract (arXiv)

We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks. Standard approaches such as propensity weighting and matching/balancing fail in such settings due to miscalibrated propensity nets and inappropriate covariate representations, respectively. We propose a new method based on adversarial training of a weighting and a discriminator network that effectively addresses this methodological gap. This is demonstrated through new theoretical characterizations of the method as well as empirical results using both fully connected architectures to learn complex relationships and convolutional architectures to handle image confounders, showing how this new method can enable strong causal analyses in these challenging settings.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Feb 2018.

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Citing papers per year: 2021: 6; 2022: 11; 2023: 8; 2024: 8; 2025: 8; 2026: 1.

42 papers.

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  1. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
  2. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 54
  3. 2021Causal Effect Inference for Structured Treatmentscited by 38
  4. 2023Optimal Transport for Treatment Effect Estimationcited by 32
  5. 2022Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimationcited by 25
  6. 2022Image-based Treatment Effect Heterogeneitycited by 20
  7. 2024Neural Causal Abstractionscited by 20
  8. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  9. 2022Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxiescited by 13
  10. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  11. 2021Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledgecited by 10
  12. 2023Integrating Earth Observation Data into Causal Inference: Challenges and Opportunitiescited by 8
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  1. 2021Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effectscited by 7
  2. 2024Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothingcited by 7
  3. 2022Neural Score Matching for High-Dimensional Causal Inferencecited by 6
  4. 2024Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimationcited by 6
  5. 2022Outcome Assumptions and Duality Theory for Balancing Weightscited by 5
  6. 2021Matched sample selection with GANs for mitigating attribute confoundingcited by 4
  7. 2022Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africacited by 4
  8. 2024Implicitly Guided Design with PropEn: Match your Data to Follow the Gradientcited by 4
  9. 2023Calibrated and Conformal Propensity Scores for Causal Effect Estimationcited by 3
  10. 2024PairNet: Training with Observed Pairs to Estimate Individual Treatment Effectcited by 3
  11. 2025Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspectivecited by 3
  12. 2021Treatment Effect Estimation using Invariant Risk Minimizationcited by 2
  13. 2024DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classificationcited by 2
  14. 2022Estimating causal effects with optimization-based methods: A review and empirical comparisoncited by 1
  15. 2023Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Datacited by 1
  16. 2023Causal inference with Machine Learning-Based Covariate Representationcited by 1
  17. 2024Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variablescited by 1
  18. 2024Towards Representation Learning for Weighting Problems in Design-Based Causal Inferencecited by 1
  19. 2025Federated Instrumental Variable Analysis via Federated Generalized Method of Momentscited by 1
  20. 2022Causal Inference for Chatting Handoffcited by 0
  21. 2022Nonlinear Causal Discovery via Kernel Anchor Regressioncited by 0
  22. 2023Variational Counterfactual Prediction under Runtime Domain Corruptioncited by 0
  23. 2023Adversarial Distribution Balancing for Counterfactual Reasoningcited by 0
  24. 2025Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATEcited by 0
  25. 2025A Computational Theory for Efficient Mini Agent Evaluation with Causal Guaranteescited by 0
  26. 2025TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosiscited by 0
  27. 2025A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillationcited by 0
  28. 2025Synergizing Deconfounding and Temporal Generalization For Time-series Counterfactual Outcome Estimationcited by 0
  29. 2025PIPCFR: Pseudo-outcome Imputation with Post-treatment Variables for Individual Treatment Effect Estimationcited by 0
  30. 2026Causal Risk Minimization for High-Dimensional Treatmentscited by 0