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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
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    References listed on IDEAS

    as
    1. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, April.
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