mlsanityData to 7 Oct 2026

On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives

arXiv 1902.10286 ↗First posted 27 Feb 2019stat.ML cs.LGcited by 20

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Abstract (arXiv)

Unobserved confounding is a central barrier to drawing causal inferences from observational data. Several authors have recently proposed that this barrier can be overcome in the case where one attempts to infer the effects of several variables simultaneously. In this paper, we present two simple, analytical counterexamples that challenge the general claims that are central to these approaches. In addition, we show that nonparametric identification is impossible in this setting. We discuss practical implications, and suggest alternatives to the methods that have been proposed so far in this line of work: using proxy variables and shifting focus to sensitivity analysis.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2019.

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Citing papers per year: 2021: 7; 2022: 1; 2023: 5; 2024: 3; 2025: 1; 2026: 3.

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  1. 2021Learning latent causal graphs via mixture oraclescited by 55
  2. 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
  3. 2021Deconfounded Visual Groundingcited by 16
  4. 2021Adversarial Visual Robustness by Causal Interventioncited by 13
  5. 2024Few-shot Learner Parameterization by Diffusion Time-stepscited by 11
  6. 2023Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Differencecited by 8
  7. 2023Debiasing Recommendation by Learning Identifiable Latent Confounderscited by 7
  8. 2021Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxiescited by 4
  9. 2023Consistent End-to-End Estimation for Counterfactual Fairnesscited by 4
  10. 2025Counterfactual Situation Testing: From Single to Multidimensional Discriminationcited by 2
  11. 2022Deep Causal Reasoning for Recommendationscited by 1
  12. 2023The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimationcited by 1
8 more
  1. 2021Identification of Latent Variables From Graphical Model Residualscited by 0
  2. 2021Do-calculus enables estimation of causal effects in partially observed biomolecular pathwayscited by 0
  3. 2021Multi-treatment Effect Estimation from Biomedical Datacited by 0
  4. 2024A/B testing under Interference with Partial Network Informationcited by 0
  5. 2024Controlling for discrete unmeasured confounding in nonlinear causal modelscited by 0
  6. 2026Shrinkage priors for Bayesian Substitute Confounderscited by 0
  7. 2026Estimating the Effect of Timing on Coupon Effectivenesscited by 0
  8. 2026Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interferencecited by 0