mlsanityData to 7 Oct 2026

Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions

arXiv 1707.06422 ↗First posted 20 Jul 2017cs.LG stat.MLcited by 125

Not on the map. Why?

Abstract (arXiv)

An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can be modeled as different contexts of a single underlying system, in which each distribution corresponds to a different perturbation of the system, or in causal terms, an intervention. We focus on a class of such causal domain adaptation problems, where data for one or more source domains are given, and the task is to predict the distribution of a certain target variable from measurements of other variables in one or more target domains. We propose an approach for solving these problems that exploits causal inference and does not rely on prior knowledge of the causal graph, the type of interventions or the intervention targets. We demonstrate our approach by evaluating a possible implementation on simulated and real world data.

Key facts. Cited by 125 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2020: 1; 2021: 29; 2022: 26; 2023: 23; 2024: 17; 2025: 18; 2026: 11.

125 papers.

Sort:

By area:

  1. 2021Towards Causal Representation Learningcited by 207
  2. 2021On Calibration and Out-of-domain Generalizationcited by 134
  3. 2021Independent mechanism analysis, a new concept?cited by 96
  4. 2021Towards a Theoretical Framework of Out-of-Distribution Generalizationcited by 94
  5. 2021Zero-Shot Recommender Systemscited by 45
  6. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  7. 2021Nonlinear Invariant Risk Minimization: A Causal Approachcited by 42
  8. 2022Invariant and Transportable Representations for Anti-Causal Domain Shiftscited by 37
  9. 2023Subspace Identification for Multi-Source Domain Adaptationcited by 36
  10. 2024Unbiased Faster R-CNN for Single-source Domain Generalized Object Detectioncited by 29
  11. 2021An Information-theoretic Approach to Distribution Shiftscited by 25
  12. 2022Causal Transportability for Visual Recognitioncited by 23
38 more
  1. 2021Learning Domain Invariant Representations for Generalizable Person Re-Identificationcited by 22
  2. 2023Invariant Learning via Probability of Sufficient and Necessary Causescited by 22
  3. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  4. 2021Regularizing towards Causal Invariance: Linear Models with Proxiescited by 20
  5. 2021Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanismscited by 20
  6. 2021Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosiscited by 18
  7. 2022Exploiting Independent Instruments: Identification and Distribution Generalizationcited by 18
  8. 2021Invariant Policy Learning: A Causal Perspectivecited by 17
  9. 2023Shift-Robust Molecular Relational Learning with Causal Substructurecited by 15
  10. 2023Data Augmentations for Improved (Large) Language Model Generalizationcited by 14
  11. 2025Wasserstein-regularized Conformal Prediction under General Distribution Shiftcited by 14
  12. 2022PAC-Bayesian Domain Adaptation Bounds for Multiclass Learnerscited by 13
  13. 2022Adapting to Latent Subgroup Shifts via Concepts and Proxiescited by 12
  14. 2023Generative Causal Representation Learning for Out-of-Distribution Motion Forecastingcited by 12
  15. 2021Out-of-Distribution Generalization Analysis via Influence Functioncited by 11
  16. 2021Towards Unbiased Visual Emotion Recognition via Causal Interventioncited by 11
  17. 2023No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imagingcited by 11
  18. 2021Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentationcited by 10
  19. 2021Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledgecited by 10
  20. 2022Evaluating Robustness to Dataset Shift via Parametric Robustness Setscited by 10
  21. 2022Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatmentcited by 10
  22. 2023An Invariant Learning Characterization of Controlled Text Generationcited by 9
  23. 2023Why Do Probabilistic Clinical Models Fail To Transport Between Sites?cited by 9
  24. 2024Mind the Graph When Balancing Data for Fairness or Robustnesscited by 9
  25. 2020Causal Transfer for Imitation Learning and Decision Making under Sensor-shiftcited by 8
  26. 2022On the Generalization and Adaption Performance of Causal Modelscited by 8
  27. 2022Causal Inference via Style Transfer for Out-of-distribution Generalisationcited by 8
  28. 2023Evaluation of Induced Expert Knowledge in Causal Structure Learning by NOTEARScited by 8
  29. 2024OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transportcited by 8
  30. 2025Partial Transportability for Domain Generalizationcited by 8
  31. 2023Intervention Generalization: A View from Factor Graph Modelscited by 7
  32. 2021Model Compression for Domain Adaptation through Causal Effect Estimationcited by 6
  33. 2024Proxy Methods for Domain Adaptationcited by 6
  34. 2022Invariant Ancestry Searchcited by 5
  35. 2022In the Eye of the Beholder: Robust Prediction with Causal User Modelingcited by 5
  36. 2023Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithmscited by 5
  37. 2023Invariance & Causal Representation Learning: Prospects and Limitationscited by 5
  38. 2021Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledgecited by 4

50 most cited of 125 All 125 (JSON)