Identifiable Latent Polynomial Causal Models Through the Lens of Change
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913589025046528 |
|---|---|
| author | Liu, Yuhang Zhang, Zhen Gong, Dong Gong, Mingming Huang, Biwei Hengel, Anton van den Zhang, Kun Shi, Javen Qinfeng |
| author_facet | Liu, Yuhang Zhang, Zhen Gong, Dong Gong, Mingming Huang, Biwei Hengel, Anton van den Zhang, Kun Shi, Javen Qinfeng |
| contents | Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability. A recent breakthrough explores identifiability by leveraging the change of causal influences among latent causal variables across multiple environments \citep{liu2022identifying}. However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models. In this paper, we extend the scope of latent causal models to involve nonlinear causal relationships, represented by polynomial models, and general noise distributions conforming to the exponential family. Additionally, we investigate the necessity of imposing changes on all causal parameters and present partial identifiability results when part of them remains unchanged. Further, we propose a novel empirical estimation method, grounded in our theoretical finding, that enables learning consistent latent causal representations. Our experimental results, obtained from both synthetic and real-world data, validate our theoretical contributions concerning identifiability and consistency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15580 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Identifiable Latent Polynomial Causal Models Through the Lens of Change Liu, Yuhang Zhang, Zhen Gong, Dong Gong, Mingming Huang, Biwei Hengel, Anton van den Zhang, Kun Shi, Javen Qinfeng Machine Learning Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability. A recent breakthrough explores identifiability by leveraging the change of causal influences among latent causal variables across multiple environments \citep{liu2022identifying}. However, this progress rests on the assumption that the causal relationships among latent causal variables adhere strictly to linear Gaussian models. In this paper, we extend the scope of latent causal models to involve nonlinear causal relationships, represented by polynomial models, and general noise distributions conforming to the exponential family. Additionally, we investigate the necessity of imposing changes on all causal parameters and present partial identifiability results when part of them remains unchanged. Further, we propose a novel empirical estimation method, grounded in our theoretical finding, that enables learning consistent latent causal representations. Our experimental results, obtained from both synthetic and real-world data, validate our theoretical contributions concerning identifiability and consistency. |
| title | Identifiable Latent Polynomial Causal Models Through the Lens of Change |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2310.15580 |