mlsanityData to 7 Oct 2026

Treatment effect estimation with disentangled latent factors

arXiv 2001.10652 ↗First posted 29 Jan 2020cs.LG stat.MLcited by 63

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Abstract (arXiv)

Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i.e., variables that affect both the treatment and the outcome. Unfortunately, this assumption is frequently violated in real-world applications, since some variables only affect the treatment but not the outcome, and vice versa. Moreover, in many cases only the proxy variables of the underlying confounding factors can be observed. In this work, we first show the importance of differentiating confounding factors from instrumental and risk factors for both average and conditional average treatment effect estimation, and then we propose a variational inference approach to simultaneously infer latent factors from the observed variables, disentangle the factors into three disjoint sets corresponding to the instrumental, confounding, and risk factors, and use the disentangled factors for treatment effect estimation. Experimental results demonstrate the effectiveness of the proposed method on a wide range of synthetic, benchmark, and real-world datasets.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2020.

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Citing papers per year: 2021: 3; 2022: 12; 2023: 16; 2024: 16; 2025: 10; 2026: 6.

63 papers.

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  1. 2022Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimizationcited by 18
  2. 2023Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencodercited by 17
  3. 2024DiffPO: A causal diffusion model for learning distributions of potential outcomescited by 17
  4. 2023Causal Effect Estimation: Recent Advances, Challenges, and Opportunitiescited by 14
  5. 2022Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxiescited by 13
  6. 2022Long-term Causal Effects Estimation via Latent Surrogates Representation Learningcited by 11
  7. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  8. 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
  9. 2022Instrumental Variables in Causal Inference and Machine Learning: A Surveycited by 10
  10. 2023Disentangled Representation for Causal Mediation Analysiscited by 10
  11. 2023Adversarially Balanced Representation for Continuous Treatment Effect Estimationcited by 10
  12. 2022Adversarial De-confounding in Individualised Treatment Effects Estimationcited by 9
38 more
  1. 2024Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inferencecited by 9
  2. 2021β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlapcited by 8
  3. 2022Causal Inference with Conditional Instruments using Deep Generative Modelscited by 6
  4. 2024Disentangled Representation Learning for Causal Inference with Instrumentscited by 6
  5. 2023Causal Inference from Text: Unveiling Interactions between Variablescited by 5
  6. 2021Towards Principled Causal Effect Estimation by Deep Identifiable Modelscited by 4
  7. 2024Conditional Generative Models are Sufficient to Sample from Any Causal Effect Estimandcited by 4
  8. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  9. 2025Counterfactual Probabilistic Diffusion with Expert Modelscited by 4
  10. 2023High Dimensional Causal Inference with Variational Backdoor Adjustmentcited by 3
  11. 2024Self-Distilled Disentangled Learning for Counterfactual Predictioncited by 3
  12. 2024Estimating Conditional Average Treatment Effects via Sufficient Representation Learningcited by 3
  13. 2025Long-Term Individual Causal Effect Estimation via Identifiable Latent Representation Learningcited by 3
  14. 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variablescited by 2
  15. 2022A Causal Research Pipeline and Tutorial for Psychologists and Social Scientistscited by 2
  16. 2023DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Datacited by 2
  17. 2023Towards Learning and Explaining Indirect Causal Effects in Neural Networkscited by 2
  18. 2024Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networkscited by 2
  19. 2021Variational Auto-Encoder Architectures that Excel at Causal Inferencecited by 1
  20. 2022NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimationcited by 1
  21. 2022Confounder Balancing for Instrumental Variable Regression with Latent Variablecited by 1
  22. 2023Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterioncited by 1
  23. 2023SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modelingcited by 1
  24. 2023Counterfactual Prediction Under Selective Confoundingcited by 1
  25. 2023Estimating treatment effects from single-arm trials via latent-variable modelingcited by 1
  26. 2024On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglementcited by 1
  27. 2024Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variablescited by 1
  28. 2025Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumptioncited by 1
  29. 2025Federated Instrumental Variable Analysis via Federated Generalized Method of Momentscited by 1
  30. 2025Deep Disentangled Representation Network for Treatment Effect Estimationcited by 1
  31. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
  32. 2025Text Rationalization for Robust Causal Effect Estimationcited by 1
  33. 2026Generalized Bayes for Causal Inferencecited by 1
  34. 2022Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Datacited by 0
  35. 2023Linking a predictive model to causal effect estimationcited by 0
  36. 2023Variational Counterfactual Prediction under Runtime Domain Corruptioncited by 0
  37. 2023Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inferencecited by 0
  38. 2024Disentangled Representation via Variational AutoEncoder for Continuous Treatment Effect Estimationcited by 0

50 most cited of 63 All 63 (JSON)