Resources

Curated references on causal DAGs, the living DAGs framework, DAGitty, and LLM-assisted review — independent of DAGpedia site mechanics. For how this project is built and run, see About DAGpedia.

Living DAGs

• Reynolds RJ. Living DAGs: the future of DAGs in epidemiology. Am J Epidemiol. 2026;195(5):1365–1367. https://doi.org/10.1093/aje/kwag029

Causal DAGs

• Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37–48. https://doi.org/10.1097/00001648-199901000-00008
• VanderWeele TJ, Hernán MA. Causal diagrams and measurement bias. Am J Epidemiol. 2012;175(7):645–652. https://doi.org/10.1093/aje/kwr431
• Hernán MA, Robins JM. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC; 2020. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/

DAGitty

• dagitty.net — browser editor and reference implementation
• Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package dagitty. Int J Epidemiol. 2016;45(6):1887–1894. https://doi.org/10.1093/ije/dyw341
• dagitty and ggdag R packages

LLM-assisted review

Large language models can flag structural issues in DAGs before human review, but should not be treated as final arbiters of scientific judgment. Planned DAGpedia validation checks (temporal ordering, collider risk, over-adjustment, and related items) are described in ADR-006.

Literature grounding

• NCBI E-utilities (PubMed) — programmatic access to abstracts for evidence grounding

Further reading

• National Academies of Sciences, Engineering, and Medicine. Fostering Integrity in Research. Washington, DC: National Academies Press; 2017. https://doi.org/10.17226/21896 — standards for transparency and reproducibility in research practice