ArticleBritish journal of anaesthesia2025
DAGs for dummies: how to extract causation from correlation.
Article in British journal of anaesthesia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Network analysis of anxiety, depression, stress symptoms and psychological resilience in shift nurses.BMC psychology · 2026Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Directed acyclic graphs (DAGs) offer a clear, structured approach to depicting causal relationships in observational research, helping to distinguish likely causation from mere association. By explicitly mapping assumptions about confounders, mediators, and colliders, DAGs guide appropriate variable selection for adjustment and help avoid common sources of bias and confounding. This transparent framework supports more rigorous causal inference and serves as a compelling complement or alternative to large randomised controlled trials. Here we introduce some foundational principles of DAGs in support of recent DAG-guided work published around the SNAP-3 project.
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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.