Computer Science ›› 2019, Vol. 46 ›› Issue (2): 178-186.doi: 10.11896/j.issn.1002-137X.2019.02.028

• Artificial Intelligence • Previous Articles     Next Articles

Transportability of Causal Information Across Different Granularities

YAO Ning1,2, MIAO Duo-qian1,2, ZHANG Zhi-fei1,3   

  1. Department of Computer Science and Technology,Tongji University,Shanghai 201804,China1
    Key Laboratory of Embedded System & Service Computing,Ministry of Education of China,Tongji University,Shanghai 201804,China2
    State Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China3
  • Received:2018-02-20 Online:2019-02-25 Published:2019-02-25

Abstract: The knowledge we learned is grain-dependent,which leads to different explanations for a phenomena at different granularities.Causality characterizes the essence of the phenomena.These factors raise an urgent problem currently to be solved in artificial intelligence:the relationship between causality and granularity as well as the transportability of causal effect at one granularity over to a different granularity.Aiming at the information system gathered from observational data,the basic graphical structures required for causal variables can be extracted directly from the data.According to these structures,the causal effects between variables can be computed.By adding new attributes to system and merging multiple information systems,the granularity in the original system is changed and then the issue of whe-ther the causal effect can be transported to the new system is settled in detail.The causal relationship from the original system cannot be transported to the new system if the new attribute acts on the effect variable,otherwise the transporta-bility is feasible in the new system.

Key words: Causal diagram, Causal relationship, Granularity, Interventions, Rough set, Transportability

CLC Number: 

  • TP18
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