IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1375-d1386850.html
   My bibliography  Save this article

Hidden Variable Discovery Based on Regression and Entropy

Author

Listed:
  • Xingyu Liao

    (Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China)

  • Xiaoping Liu

    (Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China)

Abstract

Inferring causality from observed data is crucial in many scientific fields, but this process is often hindered by incomplete data. The incomplete data can lead to mistakes in understanding how variables affect each other, especially when some influencing factors are not directly observed. To tackle this problem, we’ve developed a new algorithm called Regression Loss-increased with Causal Intensity (RLCI). This approach uses regression and entropy analysis to uncover hidden variables. Through tests on various real-world datasets, RLCI has been proven to be effective. It can help spot hidden factors that may affect the relationship between variables and determine the direction of causal relationships.

Suggested Citation

  • Xingyu Liao & Xiaoping Liu, 2024. "Hidden Variable Discovery Based on Regression and Entropy," Mathematics, MDPI, vol. 12(9), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1375-:d:1386850
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1375/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/9/1375/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1375-:d:1386850. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.