DAG resources

This page contains links to a variety of resources for those interested in learning about the use of directed acyclic graphs (DAGs) or other causal graphs for causal inference research. Many of these resources were produced by our lab, or the DAG Working Group, but the list below also includes some key classic papers by others.


DAGS 101: an introductory lecture on the basics of directed acyclic graphs and their use in biomedical research.

Selection bias and UK Biobank

Table 2 Fallacy: Or why interpretation needs more than transparency

Resolving Lord’s Paradox and Why Change-Scores Don’t Capture Change

Introduction to causal inference: Acknowledging the Third Pillar of Data Science


Causal Survival Analysis Workshop. Ellie Murray, Ellen Caniglia, & Lucia Petito. Published version.

Causal Inference in R. Workshop from Dr Malcolm Barrett.

Causal diagrams: draw your assumptions before your conclusions: Harvard X web-course from Miguel Hernán

Introductory articles

Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. Tennant PWG, Murray EJ, Arnold KF, Berrie L, Fox MP, Gadd SC, Harrison WJ, Keeble C, Ranker LR, Textor J, Tomova GD, Gilthorpe MS, Ellison GTH. International Journal of Epidemiology, Volume 50, Issue 2, April 2021, Pages 620–632

Causal Diagrams: Pitfalls and Tips. Suzuki E, Shinozaki T, Yamamoto E. Journal of Epidemiology. 2020; 30(4): 153–162.

Assessing knowledge, attitudes, and practices towards causal directed acyclic graphs: a qualitative research project. Barnard-Mayers R, Childs E, Corlin L, Caniglia EC, Fox MP, Donnelly JP, Murray EJ. European Journal of Epidemiology. 2021 Jul;36(7):659-667.

A case study and proposal for publishing directed acyclic graphs: The effectiveness of the quadrivalent human papillomavirus vaccine in perinatally HIV Infected girls. Barnard-Mayers R, Kouser H, Cohen JA, Tassiopoulos K, Caniglia EC, Moscicki A-B, Campos NG, Caunca MR, Seage GR III, Murray EJ. Journal of Clinical Epidemiology. 2022. Jan 5; S0895-4356(21)00434-0.

Complex systems models for causal inference in social epidemiology. Kouser H, Barnard-Mayers R, Murray EJ. Journal of Epidemiology & Community Health. 2021;75:702-708.

Causal Inference in R. Malcolm Barrett, Lucy D’Agostino McGowan, Travis Gerke.

Time to reality check the promises of machine learning-powered precision medicine. Wilkinson J, Arnold KF, Murray EJ, van Smeden M, Carr K, Sippy R, de Kamps M, Beam A, Konigorski S, Lippert C, Gilthorpe MS, Tennant PWG. Lancet Digital Health. 2020; 2(12):E677-80.

Intermediate & Advanced articles

A comorbid mental disorder paradox: Using causal diagrams to understand associations between posttraumatic stress disorder and suicide. Jiang T, Smith ML, Street AE, Seegulam VL, Sampson L, Murray EJ, Fox MP, Gradus JL. Psychological Trauma. 2021 Oct; 13(7):725-729.

A structural approach to selection bias. Hernán MA, Hernándex-Díaz S, Robins JM. Epidemiology. 2004; 15:615-625.

Causal directed acyclic graphs and the direction of unmeasured confounding bias. VanderWeele TJ, Hernán MA, Robins JM. Epidemiology. 2008; 19:720-728.

Results on differential and dependent measurement error of the exposure and the outcome using signed directed acyclic graphs. VanderWeele TJ, Hernán MA. American Journal of Epidemiology. 2012; 175(2): 1303-1310.

Causal diagrams for epidemiologic research. Greenland S, Pearl J, Robins JM. Epidemiology. 1999 Jan;10(1):37-48.

Causal diagrams for empirical research. Pearl J. 1995. Biometrika. 82(40):669-688.

Causal diagrams. Greenland S, Pearl J. 2007. Article on Causal Diagrams. In: Boslaugh, S. (ed.). Encyclopedia of Epidemiology. Thousand Oaks, CA: Sage Publications, 149-156.

Causal diagrams and measurement error. Hernán MA, Cole SR. 2009. American Journal of Epidemiology, Volume 170, Issue 8,Pages 959–962.

Selection bias without colliders. Hernán MA. 2017. American Journal of Epidemiology, Volume 185, Issue 11, Pages 1048–1050.

Causal Knowledge as a Prerequisite for Confounding Evaluation: An Application to Birth Defects Epidemiology. Hernán MA, Hernández-Díaz S, Werler MM, Mitchel AA. 2002. American Journal of Epidemiology; 155(2): 176-184.

Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. Textor J, van der Zander B, Gilthorpe MS, Liśkiewicz M, Ellison GTH. International Journal of Epidemiology. 2016. 45(6):1887-94.

Other causal inference articles of potential interest

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges. Arnold KG, Ellison GTH, Gadd SC, Textor J, Tennant PWG, Heppenstall A, Gilthorpe MS. Statistical Methods in Medical Research. 2018. 28(5):1347-64.

Latent class regression improves the predictive acuity and clinical utility of survival prognostication amongst chronic heart failure patients. Mbotwa JL, Kamps Md, Baxter PD, Ellison GTH, Gilthorpe MS. PLOS ON.E 2021. 16(5): e0243674.

Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. Arnold KF, Davies V, de Kamps M, Tennant PWG, Mbotwa J, Gilthorpe MS. International Journal of Epidemiology. 2020. 49(6):2074-82.

A causal inference perspective on the analysis of compositional data. Arnold KF, Berrie L, Tennant PWG, Gilthorpe MS. International Journal of Epidemiology. 2020. 49(4):1307-1313.

Commentary: Compositional data call for complex interventions. Breskin A, Murray EJ. International Journal of Epidemiology. 2020. 49(4):1314-5.

DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference. Arnold KF, Harrison WJ, Heppenstall AJ, Gilthorpe MS. International Journal of Epidemiology. 2019. 48(1): 243-53.

Adjustment for energy intake in nutritional research: a causal inference perspective. Tomova GDT, Arnold KJ, Gilthorpe MS, Tennant PWG. American Journal of Clinical Nutrition. 2021 [epub ahead of print].

Analyses of ‘change scores’ do not estimate causal effects in observational data. Tennant PWG, Arnold KF, Ellison GTH, Gilthorpe MS. International Journal of Epidemiology. 2021 [epub ahead of print].

Software & other resources

Graphical Models for Causal Inference using LaTeX. Ellie Murray.

CausalQueries R package. Alan Jacobs & Macartan Humphreys. Package guide available here: https://macartan.github.io/causalmodels/

ggdag R package for drawing DAGs. Dr Malcolm Barrett

DAGgitty — draw and analyze causal diagrams. DAG drawing software from Johannes Textor

Causal Inference: What if? Stata code for Causal Inference: what if? By Miguel Hernán and Jamie Robins