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Causal inference and longitudinal data: a case study of religion and mental health
Abstract
Purpose We provide an introduction to causal inference with longitudinal data and discuss the complexities of analysis and interpretation when exposures can vary over time. Methods We consider what types of causal questions can be addressed with the standard regression-based analyses and what types of covariate control and control for the prior values of outcome and exposure must be made to reason about causal effects. We also consider newer classes of causal models, including marginal structural models, that can assess questions of the joint effects of time-varying exposures and can take into account feedback between the exposure and outcome over time. Such feedback renders cross-sectional data ineffective for drawing inferences about causation. Results The challenges are illustrated by analyses concerning potential effects of religious service attendance on depression, in which there may in fact be effects in both directions with service attendance preventing the subsequent depression, but depression itself leading to lower levels of the subsequent religious service attendance. Conclusions Longitudinal designs, with careful control for prior exposures, outcomes, and confounders, and suitable methodology, will strengthen research on mental health, religion and health, and in the biomedical and social sciences generally.
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“Unlike a prior study, 16 we adjusted for mental health problems measured before the exposure, which is critical for reducing the influence of reverse causality.”
From Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. (2019) · cites this paper as [16]
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Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
A generalization of sampling without replacement from a finite universe
Horvitz, D. G., & Thompson, D. J. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483446 · Cited by 4,293
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
Handbook of Religion and Health
Koenig, H. G., McCullough, M. E., & Larson, D. B. (2004) · American Journal of Geriatric Psychiatry · doi:10.1097/00019442-200405000-00015 · Cited by 3,262
Constructing Inverse Probability Weights for Marginal Structural Models
Cole, S. R., & Hernan, M. A. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn164 · Cited by 2,796
Rosenbaum, P. R. (2002) · Springer series in statistics · doi:10.1007/978-1-4757-3692-2 · Cited by 2,543
Robins, J. (1986) · Mathematical Modelling · doi:10.1016/0270-0255(86)90088-6 · Cited by 2,451
Hernán, M. Á., Brumback, B., & Robins, J. M. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00012 · Cited by 1,772
Research works printed but not linked: 1
- Rothman KJ, Greenland S, Lash TL (2008) Modern epidemiology, 3rd edn. Lippincott Williams and Wilkins, Philadelphia
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Wu, Y., Xu, W., Guo, L., Li, W., Zhang, L., Gao, L., Zhu, C., Chen, S., Lin, L., & Wu, S. (2023) · Figshare · doi:10.6084/m9.figshare.23612478
Li, S., Cui, L., Cheng, J., Shu, R., Chen, S., Nguyen, U., Misra, D., Wu, S., & Gao, X. (2020) · Figshare · doi:10.6084/m9.figshare.12133779
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Self-citationLi, S., Cui, L., Cheng, J., Shu, R., Chen, S., Nguyen, U., Misra, D., Wu, S., & Gao, X. (2020) · Figshare · doi:10.6084/m9.figshare.12133746
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Yaakobi, E., Shalev, I., & Malul, S. (2025) · Group Processes & Intergroup Relations · doi:10.1177/13684302251394642
Tayyab, S., & Jabeen, S. (2025) · Journal of Professional & Applied Psychology · doi:10.52053/jpap.v6i4.438 · Cited by 1
Heyman, M., Zeng, W., & Duffy, L. (2026) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2026.105211
Gasparini, A., Crowther, M. J., & Schaffer, J. M. (2026) · BMC Medical Research Methodology · doi:10.1186/s12874-026-02782-8
Ni, Y., Chen, Y., Xiao, X., Ck, W., Stewart, S. M., & Ni, M. Y. (2026) · Child and Adolescent Mental Health · doi:10.1111/camh.70073
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Sandhu, H., & Stroope, S. (2026) · Socius Sociological Research for a Dynamic World · doi:10.1177/23780231261416210
Levin, J., Bradshaw, M., & Johnson, B. L. (2025) · Mental Health Religion & Culture · doi:10.1080/13674676.2025.2532642
Fukusada, M., Saito, M., Watanabe, R., Kobayashi, S., Ide, K., & Kondo, K. (2026) · Archives of Public Health · doi:10.1186/s13690-026-01931-1
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Barakat, S., Varidel, M., Eades, P., An, V., Hickie, I. B., Iorfino, F., & Maguire, S. (2026) · medRxiv · doi:10.64898/2026.07.21.26358524
Halstead, I. N., Major‐Smith, D., Heron, J., Golding, J., Iles-Caven, Y., Svob, C., & Joinson, C. J. (2026) · Religion Brain & Behavior · doi:10.1080/2153599x.2026.2684682
Etter, C., Aschauer, W., & Prandner, D. (2026) · International Journal of Sociology · doi:10.1080/00207659.2026.2730509
Metadata
- Authors
- VanderWeele, T. J., Jackson, J. W., & Li, S.
- Affiliations
- Harvard University (US)
- Corresponding author
- VanderWeele, Tyler J.
- Venue
- Social Psychiatry and Psychiatric Epidemiology
- Published
- 2016-09-08
- Volume
- 51
- Issue
- 11
- Pages
- 1457-1466
- ISSN
- 0933-7954 (ISSN-L)
- 1433-9285
- OpenAlex
- W2510473071
- Cited by
- 322
- References
- 48
- Citations by year
- 2026: 16 · 2025: 28 · 2024: 38 · 2023: 80 · 2022: 40 · 2021: 34 · 2020: 37 · 2019: 18 · 2018: 18 · 2017: 10 · 2016: 3
- FWCI
- 2.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Social Psychiatry and Psychiatric Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- John Templeton Foundation (52125)
- National Institute of Environmental Health Sciences (ES017876)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Health › Religion, Spirituality, and Psychology
- Keywords
- causal inference, longitudinal data, time-varying exposure, marginal structural models, religious service attendance, depression, religion and mental health, confounding adjustment, causal effects
- MeSH terms
- Cross-Sectional Studies, Depressive Disorder, Humans, Mental Health, Religion, Research Design
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