Study on whether marriage affects depression based on causal inference

Research Article
Open access

Study on whether marriage affects depression based on causal inference

Haoran Zhou 1* , Junliang Lu 2 , Ziyu Li 3 , Xinyi Zhang 4
  • 1 Northeastern University    
  • 2 University of Nottingham    
  • 3 Dalian University of Technology    
  • 4 Chengdu Shude High School    
  • *corresponding author 20195430@stu.neu.edu.cn
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230827
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

This paper applies causal-based machine learning algorithms to evaluate the causal effect of marriage on depression. The paper verifies the reinforcement of adopting causal inference through the relationship between causality and correlation and confounding bias and selection bias. In this paper, we firstly implement meta learner to estimate and analyse the causal effects. Considering the influence of confounding factors, we utilize two stages of least squares estimation and deep IV estimation based on instrumental variables to fully evaluate the causal effects. The evaluation of linear and nonlinear models shows different results, which is worthy of discussion in future studies. In conclusion, people in the rural region who get married are slightly less likely to get depressed in the future.

Keywords:

depression, confounding bias, selection bias, meta-learner, instrumental variable estimation

Zhou,H.;Lu,J.;Li,Z.;Zhang,X. (2023). Study on whether marriage affects depression based on causal inference. Applied and Computational Engineering,6,1661-1672.
Export citation

References

[1]. Gao, Wenfu; Lei, chun lei; Wang ligang, “ Popularizing psychology and building a healthy China” [J].Proceedings of the Chinese Academy of Sciences, 2016, 31(11): 1187-97.

[2]. NADEEM M,ALI A, BUZDAR M A. The association between muslim religiosity and young adult college students’ depression, anxiety, and stress [J]. J Relig Health, 2017, 56(4): 1170-9.

[3]. Wen Junna; Yang Xudong; Yang Yonghong, “Research Overview on equity of health resources allocation in China” [J]. Medicine and Philosophy (A), 2016, 37(06); 60-4.

[4]. KUEHNER C. Why is depression more common among women than among men?[J]. Lancet Psychiatry, 2017, 4(2): 146-58.

[5]. Luan Wenjin; Yang fan; Chuan hongli, “Self-assessment of mental health and its influencing factors among the elderly in China”[J]. 2012,42(03):75-83

[6]. DeMarie-Dreblow, Darlene. "Relation between knowledge and memory: A reminder that correlation does not imply causality." Child Development 62.3 (1991): 484-498.

[7]. Sander, Greenland. “May 2003 - Volume 14 - Issue 3 : Epidemiology.” Journals.lww.com, 8 Oct. 2002, journals.lww.com/epidem/Fulltext/2003/05000/Data.

[8]. Maclure, Malcolm, and Sebastian Schneeweiss. "Causation of bias: the episcope." Epidemiology (2001): 114-122.

[9]. S. R. Künzel, J. S. Sekhon, P. J. Bickel, and B. Yu, “Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning,” Proc. Natl. Acad. Sci. U.S.A., vol. 116, no. 10, pp. 4156–4165, Mar. 2019, doi: 10.1073/pnas.1804597116.

[10]. G. Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” in Advances in Neural Information Processing Systems, 2017, vol. 30. Accessed: Oct. 09, 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

[11]. Uecker JE. Marriage and mental health among young adults. J Health Soc Behav. 2012 Mar;53(1):67-83. doi: 10.1177/0022146511419206. Epub 2012 Feb 9. PMID: 22328171; PMCID: PMC3390929.

[12]. S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions.” arXiv, Nov. 24, 2017. doi: 10.48550/arXiv.1705.07874.

[13]. Guo, Ruocheng, et al. "A survey of learning causality with data: Problems and methods." ACM Computing Surveys (CSUR) 53.4 (2020): 1-37.

[14]. Angrist, Joshua D., and Guido W. Imbens. "Two-stage least squares estimation of average causal effects in models with variable treatment intensity." Journal of the American statistical Association 90.430 (1995): 431-442.

[15]. Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. "Identification of causal effects using instrumental variables." Journal of the American statistical Association 91.434 (1996): 444-455.

Cite this article

Zhou,H.;Lu,J.;Li,Z.;Zhang,X. (2023). Study on whether marriage affects depression based on causal inference. Applied and Computational Engineering,6,1661-1672.

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 volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Gao, Wenfu; Lei, chun lei; Wang ligang, “ Popularizing psychology and building a healthy China” [J].Proceedings of the Chinese Academy of Sciences, 2016, 31(11): 1187-97.

[2]. NADEEM M,ALI A, BUZDAR M A. The association between muslim religiosity and young adult college students’ depression, anxiety, and stress [J]. J Relig Health, 2017, 56(4): 1170-9.

[3]. Wen Junna; Yang Xudong; Yang Yonghong, “Research Overview on equity of health resources allocation in China” [J]. Medicine and Philosophy (A), 2016, 37(06); 60-4.

[4]. KUEHNER C. Why is depression more common among women than among men?[J]. Lancet Psychiatry, 2017, 4(2): 146-58.

[5]. Luan Wenjin; Yang fan; Chuan hongli, “Self-assessment of mental health and its influencing factors among the elderly in China”[J]. 2012,42(03):75-83

[6]. DeMarie-Dreblow, Darlene. "Relation between knowledge and memory: A reminder that correlation does not imply causality." Child Development 62.3 (1991): 484-498.

[7]. Sander, Greenland. “May 2003 - Volume 14 - Issue 3 : Epidemiology.” Journals.lww.com, 8 Oct. 2002, journals.lww.com/epidem/Fulltext/2003/05000/Data.

[8]. Maclure, Malcolm, and Sebastian Schneeweiss. "Causation of bias: the episcope." Epidemiology (2001): 114-122.

[9]. S. R. Künzel, J. S. Sekhon, P. J. Bickel, and B. Yu, “Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning,” Proc. Natl. Acad. Sci. U.S.A., vol. 116, no. 10, pp. 4156–4165, Mar. 2019, doi: 10.1073/pnas.1804597116.

[10]. G. Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” in Advances in Neural Information Processing Systems, 2017, vol. 30. Accessed: Oct. 09, 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html

[11]. Uecker JE. Marriage and mental health among young adults. J Health Soc Behav. 2012 Mar;53(1):67-83. doi: 10.1177/0022146511419206. Epub 2012 Feb 9. PMID: 22328171; PMCID: PMC3390929.

[12]. S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions.” arXiv, Nov. 24, 2017. doi: 10.48550/arXiv.1705.07874.

[13]. Guo, Ruocheng, et al. "A survey of learning causality with data: Problems and methods." ACM Computing Surveys (CSUR) 53.4 (2020): 1-37.

[14]. Angrist, Joshua D., and Guido W. Imbens. "Two-stage least squares estimation of average causal effects in models with variable treatment intensity." Journal of the American statistical Association 90.430 (1995): 431-442.

[15]. Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. "Identification of causal effects using instrumental variables." Journal of the American statistical Association 91.434 (1996): 444-455.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.