Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (2): 198-203.doi: 10.11896/jsjkx.210100053

• Database & Big Data & Data Science • Previous Articles     Next Articles

Mining Causality via Information Bottleneck

QIAO Jie1, CAI Rui-chu1, HAO Zhi-feng2   

  1. 1 School of Computer,Guangdong University of Technology,Guangzhou 510006,China
    2 School of Mathematics and Big Data,Foshan University,Foshan,Guangdong 528000,China
  • Received:2021-01-07 Revised:2021-06-01 Online:2022-02-15 Published:2022-02-23
  • About author:QIAO Jie,born in 1993,Ph.D student.His main research interests include machine learning and causality.
    CAI Rui-chu,born in 1983,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intellectual and causality.
  • Supported by:
    National Natural Science Foundation of China(61876043,61976052).

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Abstract: 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.

Key words: Causal discovery, Causal information bottleneck, Information bottleneck, Mining causality, Variational autoencoder

CLC Number: 

  • TP301.6
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