# Domain adaptation under structural causal models

arXiv: https://arxiv.org/abs/2010.15764 · HTML: https://mlsanity.com/paper/2010.15764/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 28 of 28)

- [MSeg: A Composite Dataset for Multi-domain Semantic Segmentation](https://mlsanity.com/paper/2112.13762/) (2021)
- [Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization](https://mlsanity.com/paper/2202.06856/) (2022)
- [Out-of-distribution Generalization with Causal Invariant Transformations](https://mlsanity.com/paper/2203.11528/) (2022)
- [Invariant and Transportable Representations for Anti-Causal Domain Shifts](https://mlsanity.com/paper/2207.01603/) (2022)
- [Subspace Identification for Multi-Source Domain Adaptation](https://mlsanity.com/paper/2310.04723/) (2023)
- [Interpolation can hurt robust generalization even when there is no noise](https://mlsanity.com/paper/2108.02883/) (2021)
- [Adapting to Latent Subgroup Shifts via Concepts and Proxies](https://mlsanity.com/paper/2212.11254/) (2022)
- [An Invariant Learning Characterization of Controlled Text Generation](https://mlsanity.com/paper/2306.00198/) (2023)
- [Training-Conditional Coverage Bounds under Covariate Shift](https://mlsanity.com/paper/2405.16594/) (2024)
- [Intervention Generalization: A View from Factor Graph Models](https://mlsanity.com/paper/2306.04027/) (2023)
- [Learning under random distributional shifts](https://mlsanity.com/paper/2306.02948/) (2023)
- [Prominent Roles of Conditionally Invariant Components in Domain Adaptation: Theory and Algorithms](https://mlsanity.com/paper/2309.10301/) (2023)
- [Unified Source-Free Domain Adaptation](https://mlsanity.com/paper/2403.07601/) (2024)
- [Learning Invariant Representations under General Interventions on the Response](https://mlsanity.com/paper/2208.10027/) (2022)
- [STAT: Towards Generalizable Temporal Action Localization](https://mlsanity.com/paper/2404.13311/) (2024)
- [Transfer Learning for Causal Effect Estimation](https://mlsanity.com/paper/2305.09126/) (2023)
- [Achievable distributional robustness when the robust risk is only partially identified](https://mlsanity.com/paper/2502.02710/) (2025)
- [Multi-dimensional domain generalization with low-rank structures](https://mlsanity.com/paper/2309.09555/) (2023)
- [SSL Framework for Causal Inconsistency between Structures and Representations](https://mlsanity.com/paper/2310.18634/) (2023)
- [A General Representation-Based Approach to Multi-Source Domain Adaptation](https://mlsanity.com/paper/2604.23790/) (2026)
- [A Scalable and Efficient Iterative Method for Copying Machine Learning Classifiers](https://mlsanity.com/paper/2302.02667/) (2023)
- [Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach](https://mlsanity.com/paper/2312.09758/) (2023)
- [Extending Multi-Source Bayesian Optimization With Causality Principles](https://mlsanity.com/paper/2602.14791/) (2026)
- [FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality](https://mlsanity.com/paper/2404.01608/) (2024)
- [Hybrid Causal Identification and Causal Mechanism Clustering](https://mlsanity.com/paper/2507.21792/) (2025)
- [An Expectation-Maximization Algorithm for Domain Adaptation in Gaussian Causal Models](https://mlsanity.com/paper/2601.03459/) (2026)
- [How Useful is Causal Invariance for Domain Adaptation in Finite-Sample Settings?](https://mlsanity.com/paper/2606.12680/) (2026)
- [Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS](https://mlsanity.com/paper/2607.18209/) (2026)

## Abstract (arXiv)

Domain adaptation (DA) arises as an important problem in statistical machine learning when the source data used to train a model is different from the target data used to test the model. Recent advances in DA have mainly been application-driven and have largely relied on the idea of a common subspace for source and target data. To understand the empirical successes and failures of DA methods, we propose a theoretical framework via structural causal models that enables analysis and comparison of the prediction performance of DA methods. This framework also allows us to itemize the assumptions needed for the DA methods to have a low target error. Additionally, with insights from our theory, we propose a new DA method called CIRM that outperforms existing DA methods when both the covariates and label distributions are perturbed in the target data. We complement the theoretical analysis with extensive simulations to show the necessity of the devised assumptions. Reproducible synthetic and real data experiments are also provided to illustrate the strengths and weaknesses of DA methods when parts of the assumptions in our theory are violated.