Mining Causality via Information Bottleneck
| Autor: | QIAO Jie, CAI Rui-chu, HAO Zhi-feng |
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| Jazyk: | čínština |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Jisuanji kexue, Vol 49, Iss 2, Pp 198-203 (2022) |
| Druh dokumentu: | article |
| ISSN: | 1002-137X 21010005 |
| DOI: | 10.11896/jsjkx.210100053 |
| Popis: | Causal discovery from observational data is a fundamental problem in many disciplines.However,existing methods such as constraint-based methods and causal function-based methods have strong assumptions on the causal mechanism of data,and are only applicable to low-dimensional data,and cannot be applied to scenarios with hidden variables.To this end,we propose a causality discovery method using information bottlenecks,called causal information bottleneck.This method divides the causal mechanism into two stages:compression and extraction.In the compression stage,we assume that there is a compressed hidden variable in the middle,while in the extraction stage,we extract the correlated information from effect variable as much as possible.Based on the causal information bottleneck,by deriving its variational upper bound,a causality discovery method based on the variational autoencoder is designed.The experimental results shows that the information bottleneck based method improves the accuracy by 10% in synthetic data and 4% in real world data. |
| Databáze: | Directory of Open Access Journals |
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