Quantifying direct associations between variables
Correctly quantifying the direct association between variables based on observed data is a valuable topic to study. On the one hand, many traditional methods can only measure the linear direct association. On the other hand, certain existing measures of direct association between two variables suffe...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-07-01
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| Series: | Fundamental Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667325823002212 |
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| _version_ | 1849245600381403136 |
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| author | Minyuan Zhao Yun Chen Qin Liu Shengjun Wu |
| author_facet | Minyuan Zhao Yun Chen Qin Liu Shengjun Wu |
| author_sort | Minyuan Zhao |
| collection | DOAJ |
| description | Correctly quantifying the direct association between variables based on observed data is a valuable topic to study. On the one hand, many traditional methods can only measure the linear direct association. On the other hand, certain existing measures of direct association between two variables suffer an instability problem when a parent variable has a strong influence on both variables. To solve these issues, we propose a measure, namely the independent conditional mutual information (ICMI), to quantify the direct association between two variables in a three-variable network. Additionally, we use simulation data to numerically compare the stability and reliability of the ICMI with those of other measures of direct association under different conditions. The numerical results show that ICMI performs more stably in many cases than the known measures such as unique information, conditional mutual information, and partial correlation. The statistical power results show that ICMI is more reliable for different forms of function. We further use our measure to analyze a network consisting of family finance, social security, and the residence of senior citizens. |
| format | Article |
| id | doaj-art-4d0a9e5656fa439c8c4e8331f987c1c6 |
| institution | Kabale University |
| issn | 2667-3258 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Fundamental Research |
| spelling | doaj-art-4d0a9e5656fa439c8c4e8331f987c1c62025-08-20T03:58:45ZengKeAi Communications Co. Ltd.Fundamental Research2667-32582025-07-01541538154610.1016/j.fmre.2023.06.012Quantifying direct associations between variablesMinyuan Zhao0Yun Chen1Qin Liu2Shengjun Wu3Institute for Brain Sciences and Kuang Yaming Honors School, Nanjing University, Nanjing 210023, ChinaSchool of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, ChinaSchool of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, ChinaCorresponding author.; Institute for Brain Sciences and Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China; School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, ChinaCorrectly quantifying the direct association between variables based on observed data is a valuable topic to study. On the one hand, many traditional methods can only measure the linear direct association. On the other hand, certain existing measures of direct association between two variables suffer an instability problem when a parent variable has a strong influence on both variables. To solve these issues, we propose a measure, namely the independent conditional mutual information (ICMI), to quantify the direct association between two variables in a three-variable network. Additionally, we use simulation data to numerically compare the stability and reliability of the ICMI with those of other measures of direct association under different conditions. The numerical results show that ICMI performs more stably in many cases than the known measures such as unique information, conditional mutual information, and partial correlation. The statistical power results show that ICMI is more reliable for different forms of function. We further use our measure to analyze a network consisting of family finance, social security, and the residence of senior citizens.http://www.sciencedirect.com/science/article/pii/S2667325823002212Direct associationConditional mutual informationIndependent conditional mutual informationChain graphDirected acyclic graph |
| spellingShingle | Minyuan Zhao Yun Chen Qin Liu Shengjun Wu Quantifying direct associations between variables Fundamental Research Direct association Conditional mutual information Independent conditional mutual information Chain graph Directed acyclic graph |
| title | Quantifying direct associations between variables |
| title_full | Quantifying direct associations between variables |
| title_fullStr | Quantifying direct associations between variables |
| title_full_unstemmed | Quantifying direct associations between variables |
| title_short | Quantifying direct associations between variables |
| title_sort | quantifying direct associations between variables |
| topic | Direct association Conditional mutual information Independent conditional mutual information Chain graph Directed acyclic graph |
| url | http://www.sciencedirect.com/science/article/pii/S2667325823002212 |
| work_keys_str_mv | AT minyuanzhao quantifyingdirectassociationsbetweenvariables AT yunchen quantifyingdirectassociationsbetweenvariables AT qinliu quantifyingdirectassociationsbetweenvariables AT shengjunwu quantifyingdirectassociationsbetweenvariables |