ArticleJournal of epidemiology2020
Causal Diagrams: Pitfalls and Tips.
Article in Journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social Relationships and Tooth Loss in Adults Aged 60 Years and Older: A Systematic Review and Meta-Analysis.Community dentistry and oral epidemiology · 2025Pooled it
- Real-world effectiveness of HPV vaccination against high-risk type HPV DNA positivity among adult women aged 18 - 45years in China: A matched test-negative design study.Human vaccines & immunotherapeutics · 2026Article
- Causal diagrams integrating counterfactuals and sufficient causes.American journal of epidemiology · 2026Article
- An Overarching Conceptual Framework for Menstrual Health in Sport Research: Theory, Causality and Validation.Sports medicine (Auckland, N.Z.) · 2026Review
- Association of current Statin use with incident age-related macular degeneration: using time-varying propensity scores.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Causal Approaches to Disease Progression Analyses.Epidemiology (Cambridge, Mass.) · 2025Article
- Pentavalent rotavirus vaccine effectiveness among children in Shenzhen, China: A population-based test-negative design with directed acyclic graphs bias adjustment.Infectious medicine · 2025Article
- Can algorithms replace expert knowledge for causal inference? A case study on novice use of causal discovery.American journal of epidemiology · 2025Article
- Unraveling Causality: Innovations in Epidemiologic Methods.JMA journal · 2025Article
- How to develop causal directed acyclic graphs for observational health research: a scoping review.Health psychology review · 2025Article
- New Special Article Series: "Methodological Tutorial Series for Epidemiological Studies".Journal of epidemiology · 2025Article
- Illustrating the structures of bias from immortal time using directed acyclic graphs.International journal of epidemiology · 2024Article
- Causal inference and machine learning in endocrine epidemiology.Endocrine journal · 2024Review
- Athletic Injury Research: Frameworks, Models and the Need for Causal Knowledge.Sports medicine (Auckland, N.Z.) · 2024Article
- Association between filaggrin gene mutations and the clinical features of molluscum contagiosum: The Yamanashi Adjunct Study of the Japan Environment and Children's Study.The Journal of dermatology · 2024Article
- Levels of evidence for human system risk evaluation.NPJ microgravity · 2024Review
- Causal relationships between pain, medical treatments, and knee osteoarthritis: A graphical causal model to guide analyses.Osteoarthritis and cartilage · 2024Article
- Methods in causal inference. Part 2: Interaction, mediation, and time-varying treatments.Evolutionary human sciences · 2024Article
- Methods in causal inference. Part 3: measurement error and external validity threats.Evolutionary human sciences · 2024Article
- Methods in causal inference. Part 1: causal diagrams and confounding.Evolutionary human sciences · 2024Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) "Non-manipulable" variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions.
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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.