Cited by 2,295Advanced Causal Inference TechniquesBayesian Modeling and Causal InferenceStatistical Methods in Epidemiology

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Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

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Textor, Johannes, van der Zander, Benito, Gilthorpe, Mark S., Liśkiewicz, Maciej, Ellison, George T.H.·2017·International Journal of Epidemiology

DOI 10.1093/ije/dyw341

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Abstract

Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package 'dagitty', which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package 'dagitty' can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate 'statistically equivalent' but causally different DAGs; and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs. The R package 'dagitty' is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application 'DAGitty' is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http://dagitty.net/].

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“We also drew a directed acyclic graph (DAG) [26] to select variables for our final adjusted model which included: history of being referred, use of herbs during labour, history of rupture of membranes and level of lactate in maternal blood, and duration of labour details are at http://dagitty.net/ mYJiolI .”
Method
Neighbour home gardening predicts dietary diversity among rural Tanzanian women
Blakstad, M. M., Bellows, A. L., Mosha, D., Canavan, C. R., Mlalama, K., Kinabo, J., Kruk, M. E., Masanja, H., & Fawzi, W. W. (2019) · Public Health Nutrition · doi:10.1017/s1368980018003798 · Cited by 16
Personality and the Self
Robinson, M. D., & Sedikides, C. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822.031 · Cited by 18
Conducting descriptive epidemiology and causal inference studies using observational data: A 10-point primer for stroke researchers
Churilov, L. P., Hayward, K. S., Yogendrakumar, V., & Andrew, N. E. (2025) · European Stroke Journal · doi:10.1177/23969873251332118 · Cited by 15
“30 The related webpage also provides a list of valuable resources for further leaning about DAGs and their role in articulating and communicating causal hypotheses and preventing confounding.”
The Five-Factor Model of Personality: Consensus and Controversy
McCrae, R. R. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822.013 · Cited by 20
Feeling reassured after a consultation does not reduce disability or healthcare use in people with acute low back pain: a mediation analysis of a randomised trial
Cashin, A. G., Lee, H., Traeger, A. C., Hübscher, M., Skinner, I., & McAuley, J. H. (2021) · Journal of physiotherapy · doi:10.1016/j.jphys.2021.06.007 · Cited by 15
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Negro, B. D., Lauridsen, E., Mendes, F. M., Andreasen, J. O., Wanderley, M. T., & Hermann, N. V. (2021) · Community Dentistry And Oral Epidemiology · doi:10.1111/cdoe.12686 · Cited by 15
Maternal Obesity Before Pregnancy Predicts Offspring Blood Pressure at 18 Years of Age – A Causal Mediation Analysis
Brunton, N. M., Dufault, B., Dart, A. B., Azad, M. B., & McGavock, J. (2021) · International Journal of Obesity · doi:10.1038/s41366-021-00930-2 · Cited by 19
Prenatal occupational disinfectant exposure and childhood allergies: the Japan Environment and Children’s study
Kojima, R., Shinohara, R., Kushima, M., Horiuchi, S., Otawa, S., Yokomichi, H., Akiyama, Y., Ooka, T., Miyake, K., & Yamagata, Z. (2022) · Occupational and Environmental Medicine · doi:10.1136/oemed-2021-108034 · Cited by 15
Nitrogen-based Bisphosphonate Use and Ovarian Cancer Risk in Women Aged 50 Years and Older
Tuesley, K., Webb, P. M., Protani, M. M., Spilsbury, K., Pearson, S., Coory, M. D., Donovan, P. J., Steer, C., Stewart, L. M., Pandeya, N., et al. (2022) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djac050 · Cited by 15
“In addition to our matching variables, we considered several other potential confounders determined using directed acyclic graphs (19).”
Are Child Domestic Workers Worse Off than Their Peers? Comparing Children in Domestic Work, Child Marriage, and Kinship Care with Biological Children of Household Heads: Evidence from Zimbabwe
Musizvingoza, R., Blagbrough, J., & Pocock, N. S. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19127405 · Cited by 15
“We selected covariates based on DAG recommended minimal adjustment sets, selecting the most parsimonious adjustment set [32].”
Method
Associations between pre-stroke physical activity and physical quality of life three months after stroke in patients with mild disability
Zirnsak, M., Meisinger, C., Linseisen, J., Ertl, M., Zickler, P., Naumann, M. K., & Kirchberger, I. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266318 · Cited by 15
Associations between Aircraft Noise Exposure and Self-Reported Sleep Duration and Quality in the United States-Based Prospective Nurses’ Health Study Cohort
Bozigar, M., Huang, T., Redline, S. S., Hart, J. E., Grady, S. T., Nguyen, D., James, P., Nicholas, B., Levy, J. I., Laden, F., et al. (2023) · Environmental Health Perspectives · doi:10.1289/ehp10959 · Cited by 17
“42,56 Based on variables identi ﬁ ed in the DAG, we additionally determined inclusion of environmental variables in ﬁ nal models after evaluating correlation magnitudes Environmental Health Perspectives 047010-2 131(4) April 2023 among these variables and whether they changed the e ﬀ ect estimates…”
Food Insecurity and Mental Health among Venezuelan Migrants and Refugees Living in Peru: Secondary Data Analysis of a Cross-Sectional Survey
Hernández‐Vásquez, A., Visconti-Lopez, F. J., Rojas-Cueva, A. C., Grendas, L. N., & Azañedo, D. (2023) · Nutrients · doi:10.3390/nu15143102 · Cited by 20
“To address potential confounding factors in the adjusted analysis, an epidemiological criterion was followed, and a Directed Acyclic Diagram (DAG) was constructed using DAGitty (Figure 2) [31].”
Method
Evidence of interactive effects of late-pregnancy exposure to air pollution and extreme temperature on preterm birth in China: a nationwide study
Xiao, X., Liu, R., Yu, Y., Zhang, Z., Knibbs, L. D., Jalaludin, B. B., Morawska, L., Dharmage, S. C., Heinrich, J., Papatheodorou, S., et al. (2023) · Environmental Research Letters · doi:10.1088/1748-9326/aceb0b · Cited by 15
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Fischman, M., Elias, A., Klein, A., Cohen, Y., Levy, Y., Azzam, Z. S., & Ghersin, I. (2023) · Journal of Gastroenterology · doi:10.1007/s00535-023-02034-2 · Cited by 15
“The causal relationships were identified using DAGitty, version 3.0 [19, 20].”
MethodInfluential
Federated causal inference based on real-world observational data sources: application to a SARS-CoV-2 vaccine effectiveness assessment
Meurisse, M., Estupiñán‐Romero, F., González-Galindo, J., Martínez-Lizaga, N., Royo-Sierra, S., Saldner, S., Dolanski-Aghamanoukjan, L., Degelsegger-Márquez, A., Soiland‐Reyes, S., Goethem, N. V., et al. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02068-3 · Cited by 14
“For developing the workflow, only open-source software, such as DAGitty [30], R and DuckDB [44], was used.”
MethodInfluential
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Zhou, L., Zhang, R., Yang, H., Zhang, S., Zhang, Y., Li, H., Chen, Y., Maimaitiyiming, M., Lin, J., Ma, Y., et al. (2024) · European Journal of Nutrition · doi:10.1007/s00394-023-03317-3 · Cited by 16
“2) [28].”
Unveiling the drivers of modal switch from motorcycles to public transport in Southeast Asia
Nguyen, S.-T., Moeinaddini, M., Saadi, I., & Cools, M. (2024) · Transportation Research Part F Traffic Psychology and Behaviour · doi:10.1016/j.trf.2024.03.009 · Cited by 15
Associations between fruit and vegetable intakes and incident depression in middle-aged and older adults from 10 diverse international longitudinal cohorts
Matison, A. P., Flood, V. M., Lam, B. C. P., Lipnicki, D. M., Tucker, K. L., Preux, P., Guerchet, M., d’Orsi, E., Quialheiro, A., Rech, C. R., et al. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.05.096 · Cited by 17
Complex temporal trends in biomass and abundance of Diptera communities driven by the impact of agricultural intensity
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“…in our models, such as temperature and precipitation, due to their potential impacts on the availability of cap-turable insects, we analysed potential model structures leading to inadequate controls (Cinelli et al., 2020) using directed acyclic graphs in DAGgitty software (Textor et al., 2016).”
Method
Epidemiology of menstrual-related absenteeism in 44 low-income and middle-income countries: a cross-sectional analysis of Multiple Indicator Cluster Surveys
Starr, M., Harding, R., Ataíde, R., Dinklage, N. V., Sinharoy, S. S., Jayasinghe, Y., Manda‐Taylor, L., Fisher, J., Braat, S., & Pasricha, S. (2025) · The Lancet Global Health · doi:10.1016/s2214-109x(24)00468-6 · Cited by 15
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Khan, F., Stewart, I. D., Howard, L., McKeever, T. M., Jones, S., Hearson, G., Braybrooke, R., Edwards, C. C., Jenkins, G. R., & Saini, G. (2019) · BMJ Open Respiratory Research · doi:10.1136/bmjresp-2019-000439 · Cited by 15
“Confounding factors are particularly important in studies that hope to offer causal inference of biomarkers on clinical outcomes,(27) and corrections to adjust for these will be applied in the analysis as proposed by the directed acyclic graph (figure 2).(28) Fixed factors in mixed models will include baseline demogra…”
Perfluoroalkyl substance exposure early in pregnancy was negatively associated with late pregnancy cortisone levels.
Dreyer, A., Jensen, R., Glintborg, D., Schmedes, A., Brandslund, I., Nielsen, F., Kyhl, H., Jensen, T., & Andersen, M. (2020) · Journal of Clinical Endocrinology and Metabolism · doi:10.1210/clinem/dgaa292 · Cited by 8
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Tore, E. C., Antoniou, E. E., de Groot, R. H. M., Gielen, M., Godschalk, R., Roumeliotaki, T., Smits, L. J. M., Southwood, T. R. E., Spaanderman, M. E. A., Stratakis, N., et al. (2020) · Maternal and Child Health Journal · doi:10.1007/s10995-020-02962-y · Cited by 15
Pooled analysis of genotoxicity markers in relation to exposure in the Flemish Environment and Health Studies (FLEHS) between 1999 and 2018
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“We used DAGitty for model selection, and the recommended confounders to adjust for in the analysis were BMI, HbA1c and age (Supplementary Fig.”
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Authors: Textor, J., van der Zander, B., Gilthorpe, M. S., Liśkiewicz, M., & Ellison, G. T. H.
Affiliations: Radboud University Medical Center (NL)
University of Leeds (GB)
University of Lübeck (DE)
Corresponding author: Textor, Johannes
ORCID: Textor, Johannes
Gilthorpe, Mark S.
Liśkiewicz, Maciej
Ellison, George T.H.
Venue: International Journal of Epidemiology
Publisher: Oxford University Press (OUP)
Published: 2017-01-15
Volume: 45
Issue: 6
Pages: dyw341
ISSN: 0300-5771 (ISSN-L)
1464-3685
DOI: 10.1093/ije/dyw341
OpenAlex: W2576536258
Cited by: 2,295
References: 19
Citations by year: 2026: 156 · 2025: 409 · 2024: 451 · 2023: 392 · 2022: 323 · 2021: 256 · 2020: 201 · 2019: 74 · 2018: 22 · 2017: 6
FWCI: 35.46
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: International Journal of Epidemiology (publishedVersion)
PubMed (publishedVersion)
White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
Radboud Repository (Radboud University) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: directed acyclic graphs, causal inference, R package, adjustment sets, confounding
MeSH terms: Data Interpretation, Statistical, Epidemiologic Methods, Humans, Software, Bias, Causality, Confounding Factors, Epidemiologic

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Robust causal inference using directed acyclic graphs: the R package ‘dagitty’

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