Representation learning with CGAN for casual inference
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910511265742848 |
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| author | Weng, Zhaotian Hong, Jianbo Wang, Lan |
| author_facet | Weng, Zhaotian Hong, Jianbo Wang, Lan |
| contents | Conditional Generative Adversarial Nets (CGAN) is often used to improve conditional image generation performance. However, there is little research on Representation learning with CGAN for causal inference. This paper proposes a new method for finding representation learning functions by adopting the adversarial idea. We apply the pattern of CGAN and theoretically emonstrate the feasibility of finding a suitable representation function in the context of two distributions being balanced. The theoretical result shows that when two distributions are balanced, the ideal representation function can be found and thus can be used to further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_02825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Representation learning with CGAN for casual inference Weng, Zhaotian Hong, Jianbo Wang, Lan Machine Learning I.2.6 Conditional Generative Adversarial Nets (CGAN) is often used to improve conditional image generation performance. However, there is little research on Representation learning with CGAN for causal inference. This paper proposes a new method for finding representation learning functions by adopting the adversarial idea. We apply the pattern of CGAN and theoretically emonstrate the feasibility of finding a suitable representation function in the context of two distributions being balanced. The theoretical result shows that when two distributions are balanced, the ideal representation function can be found and thus can be used to further research. |
| title | Representation learning with CGAN for casual inference |
| topic | Machine Learning I.2.6 |
| url | https://arxiv.org/abs/2407.02825 |