research-article

Causal Discovery via Causal Star Graphs

Authors:

Jing GengAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 7

Article No.: 98, Pages 1 - 24

https://doi.org/10.1145/3586997

Published: 14 April 2023 Publication History

Abstract

Discovering causal relationships among observed variables is an important research focus in data mining. Existing causal discovery approaches are mainly based on constraint-based methods and functional causal models (FCMs). However, the constraint-based method cannot identify the Markov equivalence class and the functional causal models cannot identify the complex interrelationships when multiple variables affect one variable. To address the two aforementioned problems, we propose a new graph structure Causal Star Graph (CSG) and a corresponding framework Causal Discovery via Causal Star Graphs (CD-CSG) to divide a causal directed acyclic graph into multiple CSGs for causal discovery. In this framework, we also propose a generalized learning in CSGs based on a variational approach to learn the representative intermediate variable of CSG’s non-central variables. Through the generalized learning in CSGs, the asymmetry in the forward and backward model of CD-CSG can be found to identify the causal directions in the directed acyclic graphs. We further divide the CSGs into three categories and provide the causal identification principle under each category in our proposed framework. Experiments using synthetic data show that the causal relationships between variables can be effectively identified with CD-CSG and the accuracy of CD-CSG is higher than the best existing model. By applying CD-CSG to real-world data, our proposed method can greatly augment the applicability and effectiveness of causal discovery.

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Cited By

Yi JChen Z(2024)Deconfounded Cross-modal Matching for Content-based Micro-video Background Music RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004215:3(1-25)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3650042
Zhao BWang SChi LYuan HYuan YLi QGeng JZhang S(2024)Coresets for fast causal discovery with the additive noise modelPattern Recognition10.1016/j.patcog.2023.110149148:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.110149

Index Terms

Causal Discovery via Causal Star Graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 7

August 2023

319 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3589018

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 April 2023

Online AM: 06 March 2023

Accepted: 27 February 2023

Revised: 21 September 2022

Received: 15 February 2022

Published in TKDD Volume 17, Issue 7

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National Key R&D Program of China
National Natural Science Fund of China

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Cited By

Yi JChen Z(2024)Deconfounded Cross-modal Matching for Content-based Micro-video Background Music RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004215:3(1-25)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3650042
Zhao BWang SChi LYuan HYuan YLi QGeng JZhang S(2024)Coresets for fast causal discovery with the additive noise modelPattern Recognition10.1016/j.patcog.2023.110149148:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.110149

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