Summary of Disentangle Estimation Of Causal Effects From Cross-silo Data, by Yuxuan Liu et al.
Disentangle Estimation of Causal Effects from Cross-Silo Data
by Yuxuan Liu, Haozhao Wang, Shuang Wang, Zhiming He, Wenchao Xu, Jialiang Zhu, Fan Yang
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Methodology (stat.ME)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed disentangle architecture enables seamless information exchange between private parties, facilitating unbiased estimation of local causal effects in critical fields like drug development. The innovation lies in combining shared and private branches to transmit model parameters enriched with causal mechanisms. Global constraints are introduced to mitigate bias in missing domains, leading to more accurate causal effect estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to figure out cause-and-effect relationships between different events is being developed. This is important for things like making new medicines. The problem is that the information needed to do this might be hidden away in different places and not easily shared. To solve this, an architect was designed to help share information safely. It also includes rules to make sure the results are accurate even when some data is missing. Tests on fake datasets show that it works better than other methods. |
