ORIGINAL RESEARCH article
Front. Public Health
Sec. Injury Prevention and Control
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1647200
A Causal Inference Method for Athletic Injuries Based on Quantile Threshold Functions and Latent Gaussian DAG Models
Provisionally accepted
- 1Kyungil University, Gyeongsan, Republic of Korea
- 2Shanxi Normal University, Taiyuan, China
- 3City University of Hong Kong, Hong Kong, Hong Kong, SAR China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Causal inference of athletic injuries provides the critical foundations for the development of effective prevention strategies. In recent years, the directed acyclic graph model (DAG) has established itself as an indispensable tool in the study of athletic injuries. Methods: This study proposes a quantile threshold function (QTF) and integrates it with the causal inference framework within the latent DAG model for ordinal variables. This process begins by transforming continuous variables into ordinal variables to construct a DAG, which is analyzed using the latent causal inference framework to estimate ordinal causal effects (OCE). Results: Testing this approach on real-world data showed clear differences between groups (F>52000, P<0.05). The analysis also revealed three direct paths and two indirect paths related to athletic injuries, based on the DAG. Conclusion: We obtained the OCE by intervening on variables that directly or indirectly influence athletic injuries. DAG path analysis further elucidated the impact of causal pathways on the risk of injury. The approach proposed in this study provides novel theoretical and methodological insights into athletic injuries and serves as a crucial basis for optimizing training programs and mitigating injury risk.
Keywords: causal inference, Athletic injury, Directed acyclic graph, Latent Graphical Model, Ordinal Causal Effects
Received: 15 Jun 2025; Accepted: 22 Aug 2025.
Copyright: © 2025 Xie, Hao and Xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Tao Xie, Kyungil University, Gyeongsan, Republic of Korea
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.