Contrastive self-supervised and causal inference-based contextual predictive model for international student mental health education

Yi Hu

doi:10.54254/2753-7102/2025.25791

Abstract

Drawing on 4.8 million unlabeled behavioural events from 17 universities on five continents, this study proposes the Contrastive Self-Supervised and Causal Inference-Based Contextual Predictive Model (CSCI-CPM) to forecast depression risk and quantify the value of counseling outreach for 12 438 internationally mobile students. Twin momentum-updated Transformers learn 128-dimensional, domain-invariant embeddings via an InfoNCE objective, sharply reducing label dependence and campus drift. A doubly robust head jointly models treatment propensity and counterfactual PHQ-9 outcomes, yielding unbiased individualized treatment-effect estimates. Leave-one-continent-out tests lift ROC-AUC from 0.882 to 0.931, cut root-mean-squared PHQ-9 error by 0.41, and trim PEHE to 0.027, surpassing five baselines at p < 0.01. A 16-week Thompson-sampling simulation with 25 weekly counseling slots lowers unmet-need days by 41.6 %, raises outreach to low-SES learners from 21 % to 34 %, and shrinks equal-opportunity gaps to 0.019. Real-time inference executes in 18 ms at < 0.001 kg CO₂-eq per student-day, enabling sustainable on-premise deployment. Clinician review validates 87 % of alerts, while integrated-gradients explanations highlight language-switch entropy, night-screen bursts, and weekend immobility as salient risk signals. CSCI-CPM thus offers a scalable, culturally responsive, and privacy-preserving framework for proactive mental-health governance in global higher education.

Keywords: self-supervised learning , causal inference , mental-health prediction , international students , contextual modeling

Article Information

Received: 1 August 2025

Published: 4 August 2025

DOI: https://doi.org/10.54254/2753-7102/2025.25791

Article Type: Research Article

Cite this article

Hu,Y. (2025). Contrastive self-supervised and causal inference-based contextual predictive model for international student mental health education. Advances in Social Behavior Research,16(6),131-135.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About this Volume

Volume Title: ASBR Vol.16

Part of Series: Advances in Social Behavior Research

ISSN: 2753-7102 (Print) / 2753-7110 (Online)

Copyright & License

[1]Brand, J. E., Zhou, X., & Xie, Y. (2023). Recent developments in causal inference and machine learning.Annual Review of Sociology, 49(1), 81-110.

[2]Antosz, P., Szczepanska, T., Bouman, L., Polhill, J. G., & Jager, W. (2022). Sensemaking of causality in agent-based models.International Journal of Social Research Methodology, 25(4), 557-567.

[3]Ding, T., Hasan, F., Bickel, W. K., & Pan, S. (2020). Building high performance explainable machine learning models for social media-based substance use prediction.International Journal on Artificial Intelligence Tools, 29(03n04), 2060009.

[4]Ericsson, L., Gouk, H., Loy, C. C., & Hospedales, T. M. (2022). Self-supervised representation learning: Introduction, advances, and challenges.IEEE Signal Processing Magazine,39(3), 42-62.

[5]Silva Filho, R. L. C., Brito, K., & Adeodato, P. J. L. (2023). Leveraging causal reasoning in educational data mining: an analysis of Brazilian secondary education.Applied Sciences, 13(8), 5198.

[6]Adler, D. A., Xu, X., Mishra, V., Sano, A., Kunchay, S., Abdullah, S., ... & Zhang, H. (2023). 8th International Workshop on Mental Health and Well-being: Sensing and Intervention.In Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing(pp. 784-787).

[7]González-Pérez, A., Matey-Sanz, M., Granell, C., Díaz-Sanahuja, L., Bretón-López, J., & Casteleyn, S. (2023). AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health.Journal of Biomedical Informatics, 141, 104359.

[8]Bavaresco, R., Barbosa, J., Vianna, H., Büttenbender, P., & Dias, L. (2020). Design and evaluation of a context-aware model based on psychophysiology.Computer Methods and Programs in Biomedicine, 189, 105299.

[9]de Moura, I. R., Teles, A. S., Endler, M., Coutinho, L. R., & da Silva E Silva, F. J. (2020). Recognizing context-aware human sociability patterns using pervasive monitoring for supporting mental health professionals.Sensors, 21(1), 86.

[10]Gu, S. (2025). Deep Learning-Based Prediction and Intervention Model for College Students Mental Health Status.International Journal of High Speed Electronics and Systems, 2540503.