ReviewJAMA psychiatry2020
Applying Causal Inference Methods in Psychiatric Epidemiology: A Review.
Review in JAMA psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 126 citations in OpenAlex.
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Systematic review of structured care pathways in major depressive disorder and bipolar disorder.BMC psychiatry · 2023Pooled it
- Parenting as a Mediator of Associations between Depression in Mothers and Children's Functioning: A Systematic Review and Meta-Analysis.Clinical child and family psychology review · 2020Pooled it
- Association Between Alzheimer Disease and Cancer With Evaluation of Study Biases: A Systematic Review and Meta-analysis.JAMA network open · 2020Pooled it
- Antihypertensive treatment and adverse psychiatric reactions in primary health care.Journal of human hypertension · 2026Article
- Causal inference in psychiatric research: how to critically evaluate and interpret mendelian randomization studies.Molecular psychiatry · 2026Review
- Causal inference in real-world dementia research: a systematic review protocol.Systematic reviews · 2026Article
- The causal impact of older siblings' academic achievement on younger siblings' risk for drug use disorder: instrumental variable and propensity score analyses.Psychological medicine · 2026Article
- Oral Nicotine Product Use and E-Cigarette Use Persistence in Adolescents and Young Adults: An Analysis Using Augmented Inverse Probability Weighting.American journal of epidemiology · 2026Article
- Characterizing the co-occurrence of alcohol experimentation and suicidal thoughts and behaviors in early adolescence.Translational psychiatry · 2026Article
- Causal Inference in Studies with Functional Unmasking: Psychedelics and Beyond.medRxiv : the preprint server for health sciences · 2026Article
- Concomitant Psychotropic Medication Is Associated with Reduced Outcomes of Trauma-Focused Psychotherapy for Post-Traumatic Stress Disorder.Psychotherapy and psychosomatics · 2025Article
- The first causal inference analysis of the Catalan Arthroplasty Register shows a positive effect of antibiotic-loaded bone cement on knee prosthesis survival.Journal of experimental orthopaedics · 2025Article
- Prospective associations between major depressive disorder, generalized anxiety disorder, fibromyalgia, and myalgic encephalomyelitis/chronic fatigue syndrome.Psychological medicine · 2025Article
- Hormone-sensitive depression in women: a key to precision psychiatry.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2025Article
- Causal inference methodologies to assess the effect of missed clinic visits on treatment success rate among people with tuberculosis in rural Uganda.BMC medical research methodology · 2025Article
- Elucidating the causal relationship of mechanical power and lung injury: a dynamic approach to ventilator management.Intensive care medicine experimental · 2025Article
- Enhancing causal inference in population-based neuroimaging data in children and adolescents.Developmental cognitive neuroscience · 2024Review
- Precision psychiatry needs causal inference.Acta neuropsychiatrica · 2024Review
- Equity in Digital Mental Health Interventions in the United States: Where to Next?Journal of medical Internet research · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Importance: Associations between putative risk factors and psychiatric and substance use disorders are widespread in the literature. Basing prevention efforts on such findings is hazardous. Applying causal inference methods, while challenging, is central to developing realistic and potentially actionable etiologic models for psychopathology. Observations: Causal methods can be divided into randomized clinical trials (RCTs), natural experiments, and statistical models. The first 2 approaches can potentially control for both known and unknown confounders, while statistical methods control only for known and measured confounders. The criterion standard, RCTs, can have important limitations, especially regarding generalizability. Furthermore, for ethical reasons, many critical questions in psychiatric epidemiology cannot be addressed by RCTs. We review, with examples, methods that try to meet as-if randomization assumptions, use instrumental variables, or use pre-post designs, regression discontinuity designs, or co-relative designs. Each method has strengths and limitations, especially the plausibility of as-if randomization and generalizability. Of the large family of statistical methods for causal inference, we examine propensity scoring and marginal models, which are best applied to samples with strong predictors of risk factor exposure. Conclusions and Relevance: Causal inference is important because it informs etiologic models and prevention efforts. The view that causation can be definitively resolved only with RCTs and that no other method can provide potentially useful inferences is simplistic. Rather, each method has varying strengths and limitations. We need to avoid the extremes of overzealous causal claims and the cynical view that potential causal information is unattainable when RCTs are infeasible. Triangulation, which applies different methods for elucidating causal inferences to address to the same question, may increase confidence in the resulting causal claims.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.