{"title":"DAGs for dummies: how to extract causation from correlation.","authors":"Amy Gaskell,Jamie Sleigh","doi":"10.1016/j.bja.2025.07.001","DOIUrl":null,"url":null,"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.","PeriodicalId":9250,"journal":{"name":"British journal of anaesthesia","volume":"23 1","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British journal of anaesthesia","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.bja.2025.07.001","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The British Journal of Anaesthesia (BJA) is a prestigious publication that covers a wide range of topics in anaesthesia, critical care medicine, pain medicine, and perioperative medicine. It aims to disseminate high-impact original research, spanning fundamental, translational, and clinical sciences, as well as clinical practice, technology, education, and training. Additionally, the journal features review articles, notable case reports, correspondence, and special articles that appeal to a broader audience.
The BJA is proudly associated with The Royal College of Anaesthetists, The College of Anaesthesiologists of Ireland, and The Hong Kong College of Anaesthesiologists. This partnership provides members of these esteemed institutions with access to not only the BJA but also its sister publication, BJA Education. It is essential to note that both journals maintain their editorial independence.
Overall, the BJA offers a diverse and comprehensive platform for anaesthetists, critical care physicians, pain specialists, and perioperative medicine practitioners to contribute and stay updated with the latest advancements in their respective fields.