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Causal Effect Inference with Deep Latent-Variable Models

arXiv 1705.08821 ↗First posted 24 May 2017stat.ML cs.LGcited by 408

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Abstract (arXiv)

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of confounders, factors that affect both an intervention and its outcome. A carefully designed observational study attempts to measure all important confounders. However, even if one does not have direct access to all confounders, there may exist noisy and uncertain measurement of proxies for confounders. We build on recent advances in latent variable modeling to simultaneously estimate the unknown latent space summarizing the confounders and the causal effect. Our method is based on Variational Autoencoders (VAE) which follow the causal structure of inference with proxies. We show our method is significantly more robust than existing methods, and matches the state-of-the-art on previous benchmarks focused on individual treatment effects.

Key facts. Cited by 408 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2017.

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Citing papers per year: 2019: 2; 2020: 0; 2021: 70; 2022: 74; 2023: 78; 2024: 71; 2025: 70; 2026: 43.

408 papers.

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  1. 2019Causal Confusion in Imitation Learningcited by 286
  2. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  3. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
  4. 2022Deep End-to-end Causal Inferencecited by 93
  5. 2022Amortized Inference for Causal Structure Learningcited by 89
  6. 2021On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertaintycited by 87
  7. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  8. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 82
  9. 2021On Inductive Biases for Heterogeneous Treatment Effect Estimationcited by 74
  10. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
  11. 2021Causal Influence Detection for Improving Efficiency in Reinforcement Learningcited by 70
  12. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
38 more
  1. 2022Diversify and Disambiguate: Learning From Underspecified Datacited by 55
  2. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 54
  3. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  4. 2019Task-Relevant Adversarial Imitation Learningcited by 45
  5. 2022A Causal Lens for Controllable Text Generationcited by 45
  6. 2025CausalPFN: Amortized Causal Effect Estimation via In-Context Learningcited by 43
  7. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  8. 2021Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Datacited by 42
  9. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 42
  10. 2021Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regressioncited by 40
  11. 2022Causal Imitation Learning with Unobserved Confounderscited by 40
  12. 2021Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluationcited by 39
  13. 2021Causal Effect Inference for Structured Treatmentscited by 38
  14. 2023Measuring axiomatic soundness of counterfactual image modelscited by 34
  15. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  16. 2022Learning Causal Effects on Hypergraphscited by 31
  17. 2021A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Modelscited by 29
  18. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  19. 2021Towards a Collective Agenda on AI for Earth Science Data Analysiscited by 27
  20. 2022D'ARTAGNAN: Counterfactual Video Generationcited by 27
  21. 2023Bayesian Neural Controlled Differential Equations for Treatment Effect Estimationcited by 26
  22. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  23. 2021SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Datacited by 25
  24. 2022Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimationcited by 25
  25. 2022Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimationcited by 25
  26. 2023Explicit Feature Interaction-aware Uplift Network for Online Marketingcited by 25
  27. 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
  28. 2023Self-Interpretable Time Series Prediction with Counterfactual Explanationscited by 24
  29. 2021CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoningcited by 23
  30. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  31. 2022Predicting the impact of treatments over time with uncertainty aware neural differential equationscited by 23
  32. 2021E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modelingcited by 21
  33. 2021Fairness without the sensitive attribute via Causal Variational Autoencodercited by 21
  34. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  35. 2022Image-based Treatment Effect Heterogeneitycited by 20
  36. 2022An Instrumental Variable Approach to Confounded Off-Policy Evaluationcited by 20
  37. 2024Neural Causal Abstractionscited by 20
  38. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19

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