mlsanityData to 14 Oct 2026

The Blessings of Multiple Causes

arXiv 1805.06826 ↗First posted 17 May 2018stat.ML cs.LG stat.MEcited by 104

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Abstract (arXiv)

Causal inference from observational data often assumes "ignorability," that all confounders are observed. This assumption is standard yet untestable. However, many scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. We propose the deconfounder, an algorithm that combines unsupervised machine learning and predictive model checking to perform causal inference in multiple-cause settings. The deconfounder infers a latent variable as a substitute for unobserved confounders and then uses that substitute to perform causal inference. We develop theory for the deconfounder, and show that it requires weaker assumptions than classical causal inference. We analyze its performance in three types of studies: semi-simulated data around smoking and lung cancer, semi-simulated data around genome-wide association studies, and a real dataset about actors and movie revenue. The deconfounder provides a checkable approach to estimating closer-to-truth causal effects.

Key facts. Cited by 104 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2018.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 28; 2022: 19; 2023: 19; 2024: 14; 2025: 16; 2026: 7.

104 papers.

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  1. 2019Causal Confusion in Imitation Learningcited by 288
  2. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  3. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
  4. 2022Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equationscited by 59
  5. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 43
  6. 2021Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Datacited by 42
  7. 2022Anticipating Performativity by Predicting from Predictionscited by 37
  8. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  9. 2021Identifiable Generative Models for Missing Not at Random Data Imputationcited by 30
  10. 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
  11. 2021Counterfactual Explanations for Arbitrary Regression Modelscited by 22
  12. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
38 more
  1. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 18
  2. 2023Zero-shot causal learningcited by 17
  3. 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
  4. 2021The Blessings of Unlabeled Background in Untrimmed Videoscited by 16
  5. 2021Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxiescited by 16
  6. 2021Deconfounded Visual Groundingcited by 16
  7. 2023CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systemscited by 16
  8. 2024Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatmentscited by 16
  9. 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learningcited by 15
  10. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  11. 2021NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatmentscited by 14
  12. 2021Deconfounded Causal Collaborative Filteringcited by 14
  13. 2022Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxiescited by 13
  14. 2025Black Box Causal Inference: Effect Estimation via Meta Predictioncited by 13
  15. 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 13
  16. 2021Intact-VAE: Estimating Treatment Effects under Unobserved Confoundingcited by 10
  17. 2023Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounderscited by 10
  18. 2021Causal Estimation with Functional Confounderscited by 9
  19. 2021Obtaining Causal Information by Merging Datasets with MAXENTcited by 9
  20. 2021Causal Inference Despite Limited Global Confounding via Mixture Modelscited by 9
  21. 2023Why Do Probabilistic Clinical Models Fail To Transport Between Sites?cited by 9
  22. 2021Causal Effect Variational Autoencoder with Uniform Treatmentcited by 8
  23. 2025DeCaFlow: A deconfounding causal generative modelcited by 8
  24. 2022Mitigating Health Disparities in EHR via Deconfoundercited by 7
  25. 2023Debiasing Recommendation by Learning Identifiable Latent Confounderscited by 7
  26. 2023Delphic Offline Reinforcement Learning under Nonidentifiable Hidden Confoundingcited by 7
  27. 2024Causal Contrastive Learning for Counterfactual Regression Over Timecited by 7
  28. 2022Estimating Social Influence from Observational Datacited by 5
  29. 2023Scalable Computation of Causal Boundscited by 5
  30. 2024What is Fair? Defining Fairness in Machine Learning for Healthcited by 5
  31. 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learningcited by 5
  32. 2021Towards Principled Causal Effect Estimation by Deep Identifiable Modelscited by 4
  33. 2021Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxiescited by 4
  34. 2022Generative multitask learning mitigates target-causing confoundingcited by 4
  35. 2022Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Futurecited by 4
  36. 2024Self-Distilled Disentangled Learning for Counterfactual Predictioncited by 4
  37. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  38. 2021Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implicationcited by 3

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