# Learning causal representations for robust domain adaptation

arXiv: https://arxiv.org/abs/2011.06317 · HTML: https://mlsanity.com/paper/2011.06317/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 18 of 18)

- [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021)
- [Structure Learning with Continuous Optimization: A Sober Look and Beyond](https://mlsanity.com/paper/2304.02146/) (2023)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences](https://mlsanity.com/paper/2305.05986/) (2023)
- [Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting](https://mlsanity.com/paper/2302.08635/) (2023)
- [Towards Privacy-Aware Causal Structure Learning in Federated Setting](https://mlsanity.com/paper/2211.06919/) (2022)
- [Instrumental Variable-Driven Domain Generalization with Unobserved Confounders](https://mlsanity.com/paper/2110.01438/) (2021)
- [The Robustness of Differentiable Causal Discovery in Misspecified Scenarios](https://mlsanity.com/paper/2510.12503/) (2025)
- [Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems](https://mlsanity.com/paper/2404.13348/) (2024)
- [Towards Human-like Perception: Learning Structural Causal Model in Heterogeneous Graph](https://mlsanity.com/paper/2312.05757/) (2023)
- [Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyond](https://mlsanity.com/paper/2410.18396/) (2024)
- [Active Inference AI Systems for Scientific Discovery](https://mlsanity.com/paper/2506.21329/) (2025)
- [Linking a predictive model to causal effect estimation](https://mlsanity.com/paper/2304.04566/) (2023)
- [A Deep Probabilistic Flow-Based Framework for Unsupervised Cross-Domain Soft Sensing](https://mlsanity.com/paper/2306.04919/) (2023)
- [Implicit Causal Representation Learning via Switchable Mechanisms](https://mlsanity.com/paper/2402.11124/) (2024)
- [Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learning](https://mlsanity.com/paper/2408.16577/) (2024)
- [Un-mixing Test-time Adaptation under Heterogeneous Data Streams](https://mlsanity.com/paper/2411.15173/) (2024)
- [Importance Ranking in Complex Networks via Influence-aware Causal Node Embedding](https://mlsanity.com/paper/2511.01228/) (2025)

## Abstract (arXiv)

Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all existing domain adaptation methods heavily require large amounts of unlabeled target domain data for learning domain invariant representations to achieve good generalizability on the target domain. In fact, in many real-world applications, target domain data may not always be available. In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation. To tackle this problem, under the assumption that causal relationships between features and the class variable are robust across domains, we propose a novel Causal AutoEncoder (CAE), which integrates deep autoencoder and causal structure learning into a unified model to learn causal representations only using data from a single source domain. Specifically, a deep autoencoder model is adopted to learn low-dimensional representations, and a causal structure learning model is designed to separate the low-dimensional representations into two groups: causal representations and task-irrelevant representations. Using three real-world datasets the extensive experiments have validated the effectiveness of CAE compared to eleven state-of-the-art methods.