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Causal Discovery on Non-Euclidean Data

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Ning AnAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 2202 - 2211

https://doi.org/10.1145/3534678.3539485

Published: 14 August 2022 Publication History

Abstract

Researchers recently started developing deep learning models capable of handling non-Euclidean data. However, because of existing framework limitations on model representations and learning algorithms, few have explored causal discovery on non-Euclidean data. This paper is the first attempt to do so. We start by proposing the Non-Euclidean Causal Model (NECM) which describes the causal generative relationship of non-Euclidean data and creates a new tensor data type along with a mapping process for the non-Euclidean causal mechanism. Second, within the NECM, we propose the non-Euclidean Hybrid Learning (NEHL) method, a causal discovery algorithm relying on the concept of the ball covariance recently introduced in the statistics field. Third, we generate two types of non-Euclidean datasets: Functional Data and Symmetric Positive Definite manifold data in conformity with the NECM. Finally, experimental results on the generated data and real-world data demonstrate the effectiveness of the proposed NEHL method.

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Cited By

Yang HHu SGeng JHuang THu JZhang HZhu Q(2024)Heterogeneous graph-based knowledge tracing with spatiotemporal evolutionExpert Systems with Applications10.1016/j.eswa.2023.122249238(122249)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.122249
Kuai FYang J(2023)A Non-Euclidean Causal Discovery Algorithm in Metric Spaces2023 4th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI)10.1109/ICCBD-AI62252.2023.00017(53-57)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICCBD-AI62252.2023.00017

Index Terms

Causal Discovery on Non-Euclidean Data
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Learning in probabilistic graphical models
        Bayesian network models

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Published In

cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

Copyright © 2022 ACM.

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Publication History

Published: 14 August 2022

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the National Key Research and Development Program of China
the Provincial Key Research and Development Program of Anhui
the National Natural Science Foundation of China

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KDD '22

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

Washington DC, USA

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Cited By

Yang HHu SGeng JHuang THu JZhang HZhu Q(2024)Heterogeneous graph-based knowledge tracing with spatiotemporal evolutionExpert Systems with Applications10.1016/j.eswa.2023.122249238(122249)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.122249
Kuai FYang J(2023)A Non-Euclidean Causal Discovery Algorithm in Metric Spaces2023 4th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI)10.1109/ICCBD-AI62252.2023.00017(53-57)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICCBD-AI62252.2023.00017

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