mlsanityData to 14 Oct 2026

Intact-VAE: Estimating Treatment Effects under Unobserved Confounding

arXiv 2101.06662 ↗First posted 17 Jan 2021stat.ML cs.LG stat.MEcited by 10

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Abstract (arXiv)

NOTE: This preprint has a flawed theoretical formulation. Please avoid it and refer to the ICLR22 publication https://openreview.net/forum?id=q7n2RngwOM. Also, arXiv:2109.15062 contains some new ideas on unobserved Confounding. As an important problem of causal inference, we discuss the identification and estimation of treatment effects under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model (i.e., determinacy of representation), our VAE is a consistent estimator with representation balanced for treatment groups. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 28 of its 55 references that are in the map. First posted on arXiv 17 Jan 2021.

Builds on

28 of 55 references.

References · 28

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2013Auto-Encoding Variational Bayescited by 4,053
  3. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  4. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
  5. 2019An Introduction to Variational Autoencoderscited by 1,000
  6. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  7. 2019Variational Autoencoders and Nonlinear ICA: A Unifying Frameworkcited by 503
  8. 2014Semi-Supervised Learning with Deep Generative Modelscited by 496
  9. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  10. 2016Tutorial on Variational Autoencoderscited by 409
  11. 2016Learning Representations for Counterfactual Inferencecited by 329
  12. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
16 more
  1. 2018Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learningcited by 282
  2. 2016Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICAcited by 273
  3. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 158
  4. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  5. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 139
  6. 2018Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustnesscited by 108
  7. 2020On Linear Identifiability of Learned Representationscited by 105
  8. 2018The Blessings of Multiple Causescited by 104
  9. 2020Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)cited by 92
  10. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  11. 2020CausalVAE: Structured Causal Disentanglement in Variational Autoencodercited by 48
  12. 2019Using Embeddings to Correct for Unobserved Confounding in Networkscited by 31
  13. 2018Causal Inference with Noisy and Missing Covariates via Matrix Factorizationcited by 30
  14. 2017Generalizing causal inferences from individuals in randomized trials to all trial-eligible individualscited by 25
  15. 2020Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Methodcited by 16
  16. 2020A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributionscited by 7

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 4; 2024: 2.

10 papers.

  1. 2021β-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlapcited by 8
  2. 2021Towards Principled Causal Effect Estimation by Deep Identifiable Modelscited by 4
  3. 2022A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statisticscited by 4
  4. 2023Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representationcited by 3
  5. 2023High Dimensional Causal Inference with Variational Backdoor Adjustmentcited by 3
  6. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  7. 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variablescited by 2
  8. 2023SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modelingcited by 1
  9. 2023Active and Passive Causal Inference Learningcited by 1
  10. 2024Disentangled Graph Autoencoder for Treatment Effect Estimationcited by 0