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Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference

Author

Listed:
  • Rui Luo

    (Key Lab of Interior Layout optimization and Security, Chengdu Normal University, Chengdu 611130, China)

  • Lijia Sun

    (School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Yin Kuang

    (Key Lab of Interior Layout optimization and Security, Chengdu Normal University, Chengdu 611130, China)

  • Ping Deng

    (Key Lab of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 611756, China)

  • Mengna Lu

    (School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated.

Suggested Citation

  • Rui Luo & Lijia Sun & Yin Kuang & Ping Deng & Mengna Lu, 2022. "Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference," Mathematics, MDPI, vol. 10(6), pages 1-13, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:957-:d:773206
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    References listed on IDEAS

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