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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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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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