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Ordinal causal discovery based on Markov blankets

Author

Listed:
  • Yu Du

    (Xinjiang University)

  • Yi Sun

    (Xinjiang University
    Northeast Normal University)

  • Luyao Tan

    (Xinjiang University)

Abstract

This work focuses on learning causal network structures from ordinal categorical data. By combining constraint-based with score-and-search methodologies in structural learning, we propose a hybrid method called Markov Blanket Based Ordinal Causal Discovery (MBOCD) algorithm, which can capture the ordinal relationship of values in ordinal categorical variables. Theoretically, it is proved that for ordinal causal networks, two adjacent DAGs belonging to the same Markov equivalence class are identifiable, which results in the generation of a causal graph. Simulation experiments demonstrate that the proposed algorithm outperforms existing methods in terms of computational efficiency and accuracy. The code of this work is open at: https://github.com/leoydu/MBOCDcode.git .

Suggested Citation

  • Yu Du & Yi Sun & Luyao Tan, 2025. "Ordinal causal discovery based on Markov blankets," Computational Statistics, Springer, vol. 40(3), pages 1311-1335, March.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:3:d:10.1007_s00180-024-01513-1
    DOI: 10.1007/s00180-024-01513-1
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    References listed on IDEAS

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    1. Xianchao Xie & Zhi Geng, 2009. "Collapsibility for Directed Acyclic Graphs," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 185-203, June.
    2. Flaminia Musella, 2013. "A PC algorithm variation for ordinal variables," Computational Statistics, Springer, vol. 28(6), pages 2749-2759, December.
    3. Wang, Changzhang & Zhou, You & Zhao, Qiang & Geng, Zhi, 2014. "Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 252-266.
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