Disentangle Estimation of Causal Effects from Cross-Silo Data
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916080760389632 |
|---|---|
| author | Liu, Yuxuan Wang, Haozhao Wang, Shuang He, Zhiming Xu, Wenchao Zhu, Jialiang Yang, Fan |
| author_facet | Liu, Yuxuan Wang, Haozhao Wang, Shuang He, Zhiming Xu, Wenchao Zhu, Jialiang Yang, Fan |
| contents | Estimating causal effects among different events is of great importance to critical fields such as drug development. Nevertheless, the data features associated with events may be distributed across various silos and remain private within respective parties, impeding direct information exchange between them. This, in turn, can result in biased estimations of local causal effects, which rely on the characteristics of only a subset of the covariates. To tackle this challenge, we introduce an innovative disentangle architecture designed to facilitate the seamless cross-silo transmission of model parameters, enriched with causal mechanisms, through a combination of shared and private branches. Besides, we introduce global constraints into the equation to effectively mitigate bias within the various missing domains, thereby elevating the accuracy of our causal effect estimation. Extensive experiments conducted on new semi-synthetic datasets show that our method outperforms state-of-the-art baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02154 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Disentangle Estimation of Causal Effects from Cross-Silo Data Liu, Yuxuan Wang, Haozhao Wang, Shuang He, Zhiming Xu, Wenchao Zhu, Jialiang Yang, Fan Machine Learning Artificial Intelligence Cryptography and Security Methodology Estimating causal effects among different events is of great importance to critical fields such as drug development. Nevertheless, the data features associated with events may be distributed across various silos and remain private within respective parties, impeding direct information exchange between them. This, in turn, can result in biased estimations of local causal effects, which rely on the characteristics of only a subset of the covariates. To tackle this challenge, we introduce an innovative disentangle architecture designed to facilitate the seamless cross-silo transmission of model parameters, enriched with causal mechanisms, through a combination of shared and private branches. Besides, we introduce global constraints into the equation to effectively mitigate bias within the various missing domains, thereby elevating the accuracy of our causal effect estimation. Extensive experiments conducted on new semi-synthetic datasets show that our method outperforms state-of-the-art baselines. |
| title | Disentangle Estimation of Causal Effects from Cross-Silo Data |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Methodology |
| url | https://arxiv.org/abs/2401.02154 |