mlsanityData to 7 Oct 2026

Adapting to Latent Subgroup Shifts via Concepts and Proxies

arXiv 2212.11254 ↗First posted 21 Dec 2022stat.ML cs.AI cs.LGcited by 12

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Abstract (arXiv)

We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor label shift assumptions apply. We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target. The identification results are constructive, immediately suggesting an algorithm for estimating the optimal predictor in the target. For continuous observations, when this algorithm becomes impractical, we propose a latent variable model specific to the data generation process at hand. We show how the approach degrades as the size of the shift changes, and verify that it outperforms both covariate and label shift adjustment.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 31 of its 72 references that are in the map. First posted on arXiv 21 Dec 2022.

Builds on

31 of 72 references.

References · 31

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19 more
  1. 2020Concept Whitening for Interpretable Image Recognitioncited by 225
  2. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 138
  3. 2019Explaining Classifiers with Causal Concept Effect (CaCE)cited by 135
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  6. 2020Domain Adaptation with Conditional Distribution Matching and Generalized Label Shiftcited by 124
  7. 2020A Unified View of Label Shift Estimationcited by 119
  8. 2019Regularized Learning for Domain Adaptation under Label Shiftscited by 118
  9. 2020An Introduction to Proximal Causal Learningcited by 91
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  15. 2020Debiasing Concept-based Explanations with Causal Analysiscited by 32
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Built on by

Cited by year (2026 to 7 Oct)

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

12 papers.

  1. 2024A survey on Concept-based Approaches For Model Improvementcited by 8
  2. 2024Proxy Methods for Domain Adaptationcited by 6
  3. 2024UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Frameworkcited by 6
  4. 2024Scalable Out-of-distribution Robustness in the Presence of Unobserved Confounderscited by 5
  5. 2025When Shift Happens - Confounding Is to Blamecited by 5
  6. 2024Proxy-informed Bayesian transfer learning with unknown sourcescited by 2
  7. 2024Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Modelscited by 2
  8. 2025Limitations of refinement methods for weak to strong generalizationcited by 1
  9. 2023Prediction under Latent Subgroup Shifts with High-Dimensional Observationscited by 0
  10. 2024Causal Fine-Tuning under Latent Confounded Shiftcited by 0
  11. 2025Transferring Causal Effects using Proxiescited by 0
  12. 2026Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxiescited by 0