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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Ramos, J. S., Robichaud, M. V., Arsenault, J., Chorfi, Y., Costa, M., Dubuc, J., Ferraro, S., Francoz, D., Rousseau, M., Fecteau, G., et al. (2023) · Journal of Dairy Science · doi:10.3168/jds.2022-22827 · Cited by 10
Hypothetical interventions and risk of atrial fibrillation by sex and education: application of the parametric g-formula in the Tromsø Study
Nilsen, L., Sharashova, E., Løchen, M., Danaei, G., & Wilsgaard, T. (2023) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwad240 · Cited by 8
The modified Glasgow Prognostic Score indicates an increased risk of anastomotic leakage after anterior resection for rectal cancer
Golshani, P., Park, J., Häggström, J., Segelman, J., Matthiessen, P., Lydrup, M., Rutegård, M., on behalf of the RectoLeak study group, Gerdin, A., Sjöström, O., et al. (2023) · International Journal of Colorectal Disease · doi:10.1007/s00384-023-04496-5 · Cited by 8
“A directed acyclic graph [17] is presented in Fig.”
Prevalence of subclinical pulmonary tuberculosis and its association with HIV in household contacts of index tuberculosis patients in two South African provinces: a secondary, cross-sectional analysis of a cluster-randomised trial
Carter, N., Webb, E. L., Lebina, L., Motsomi, K., Bosch, Z., Martinson, N. A., & MacPherson, P. (2023) · BMC Global and Public Health · doi:10.1186/s44263-023-00022-5 · Cited by 11
“1) [24, 33, 34].”
Anxiety but not menopausal status influences the risk of long-COVID-19 syndrome in women living in Latin America
Pérez-López, F. R., Blümel, J. E., Vallejo, M. S., Rodríguez, I., Tserotas, K., Salinas, C., Rodrigues, M. A. H., Rey, C., Ojeda, E., Ñañez, M., et al. (2023) · Maturitas · doi:10.1016/j.maturitas.2023.107873 · Cited by 8
COPD in Never-Smokers: BOLD Australia Study
Ivey, M. A., Smith, S. M. S., Benke, G., Toelle, B. G., Hunter, M. L., James, A. L., Maguire, G. P., Wood-Baker, R. R., Johns, D. P., Marks, G. B., et al. (2024) · International Journal of COPD · doi:10.2147/copd.s439307 · Cited by 8
“2 software, 20 to examine the relationship between the covariates and COPD inclusive of unmeasured exposures of genetics and air pollution (Figure 1).”
Method
Personality and Politics
Caprara, G. V., & Vecchione, M. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822.038 · Cited by 9
Blood pressure and hypertension during pregnancy in women with polycystic ovary syndrome: Odense Child Cohort
Nielsen, J. H., Birukov, A., Jensen, R. C., Kyhl, H. B., Jørgensen, J. S., Andersen, M., & Glintborg, D. (2020) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13914 · Cited by 10
Relationship between family functioning and health-related quality of life among methadone maintenance patients: a Bayesian approach
Long, K. Q., Ong, P. T., Thao, T. T. K., Huy, N. V., Vu, L., Mai, V. Q., & Minh, H. V. (2020) · Quality of Life Research · doi:10.1007/s11136-020-02598-z · Cited by 9
Do-search
Karvanen, J., Tikka, S., & Hyttinen, A. (2020) · Epidemiology · doi:10.1097/ede.0000000000001270 · Cited by 10
“Graphical models can help to describe this information in an organized manner (Textor et al., 2011; Karvanen, 2015; Textor et al., 2016; Matthay and Glymour, 2020).”
Background
Associations of Father and Adult Male Presence with First Pregnancy and HIV Infection: Longitudinal Evidence from Adolescent Girls and Young Women in Rural South Africa (HPTN 068)
Albert, L. M., Edwards, J., Pence, B., Speizer, I. S., Hillis, S., Kahn, K., Gómez-Olivé, F. X., Wagner, R. G., Twine, R., & Pettifor, A. (2021) · AIDS and Behavior · doi:10.1007/s10461-020-03147-y · Cited by 8
“The R package,“DAGgity “, was used to derive the minimally sufficient covariate adjustment sets; these included age, household expenditures, whether the AGYW’s household received at least one grant, and years since orphan status [46, 47].”
Method
Trace elements in drinking water and the incidence of attention-deficit hyperactivity disorder
Thygesen, M., Schullehner, J., Hansen, B., Sigsgaard, T., Voutchkova, D. D., Kristiansen, S. M., Pedersen, C. B., & Dalsgaard, S. (2021) · Journal of Trace Elements in Medicine and Biology · doi:10.1016/j.jtemb.2021.126828 · Cited by 10
Causal inference for process understanding in Earth sciencesPreprint
Massmann, A. K., Gentine, P., & Runge, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00912 · Cited by 8
Invited commentary: where do the causal DAGS come from?
Didelez, V. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae028 · Cited by 8
Association between Mediterranean diet and metal mixtures concentrations in pregnant people from the New Hampshire Birth Cohort Study
Notario-Barandiarán, L., Signes‐Pastor, A. J., Laue, H. E., Abuawad, A. K., Jackson, B. P., Madan, J. C., & Karagas, M. R. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.169127 · Cited by 8
Dairy producers' colostrum practices, marketing, and attitudes toward male dairy calves
Cheng, T., Renaud, D. L., Proudfoot, K. L., Pempek, J. A., & Habing, G. G. (2024) · Journal of Dairy Science · doi:10.3168/jds.2023-24149 · Cited by 8
“…lactating cows, housing, milking system) and producer characteristics (e.g., age, on-farm role, education, Supplementary material 1 [S1]) were identified as likely confounders using a causal diagram (Figure 1, created by DAGitty [Textor et al., 2016]) and included as fixed effects in both models.”
Method
Pre-pregnancy ozone and ultrafine particulate matter exposure during second year of life associated with decreased cognitive and adaptive functioning at aged 2–5 years
Goodrich, A. J., Kleeman, M. J., Tancredi, D. J., Ludeña, Y., Bennett, D. H., Hertz‐Picciotto, I., & Schmidt, R. J. (2024) · Environmental Research · doi:10.1016/j.envres.2024.118854 · Cited by 7
Nutrition adequacy in the late period of the acute phase is associated with a lower risk of 30‐day mortality in critically ill patients: A prospective cohort study
Coruja, M. K., Antunes, L. D. C., Leotti, V. B., & Steemburgo, T. (2024) · Nutrition in Clinical Practice · doi:10.1002/ncp.11155 · Cited by 6
“24 The graphical criteria for selecting the adjusted covariates were used to define the minimum set of covariates to adjust for confounding and reduce variable selection bias.”
Falls and patterns of physical activity participation over 18 years in the Australian Longitudinal Study on Women’s Health
Kwok, W. S., Khalatbari‐Soltani, S., Dolja‐Gore, X., Byles, J., Oliveira, J. S., Pinheiro, M. B., Tiedemann, A., & Sherrington, C. (2024) · British Journal of Sports Medicine · doi:10.1136/bjsports-2024-108262 · Cited by 7
“21 The variables considered in the DAG are described in online supplemental table 1.”
eVisits to primary care and subsequent health care contacts: a register-based study
Glock, H., Jakobsson, U., Bolmsjö, B. B., Nymberg, V. M., Wolff, M., & Calling, S. (2024) · BMC Family Practice · doi:10.1186/s12875-024-02541-y · Cited by 7
“…for age, sex, and CNI of the patient’s registered PHCC. Type of health care professional for the eVisit was considered a mediator rather than a confounder of effects and was therefore not used for adjustment as the regression model was intended to study total effects (Additional file 1) [28].”
Method
Vine copula based structural equation models
Czado, C. (2024) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2024.108076 · Cited by 7
Kontext als impliziter und expliziter Gegenstand der Versorgungsforschung
Demirer, I., Nellessen-Martens, G., & Ansmann, L. (2024) · Springer eBooks · doi:10.1007/978-3-658-42863-1_2 · Cited by 7
Exposure to a mixture of arsenic species and growth indicators in 6–12-year-old children from the cycles 2007–2020 NHANES
García‐Villarino, M., Fernández‐Iglesias, R., García, A. V., Villa-Fernández, E., Fernández‐Arce, L., Galán, I. R., Lambert, C., Martín, V., Karagas, M. R., Delgado, E., et al. (2025) · Environment International · doi:10.1016/j.envint.2025.109347 · Cited by 8
Impact of maternal smoking and secondhand smoke exposure during singleton pregnancy on placental abruption: analysis of a prospective cohort study (the Japan Environment and Children’s Study)
Karumai-Mori, H., Hamada, H., Iwama, N., Tomita, H., Tagami, K., Kumagai, N., Kudo, R., Wang, H., Izumi, S., Watanabe, Z., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-089499 · Cited by 7
“Table 2 shows maternal and neonatal characteristics according to categories of SHS exposure during pregnancy.”
Mycoestrogen Exposure during Pregnancy: Impact of the ABCG2 Q141K Variant on Birth and Placental Outcomes
Rivera‐Núñez, Z., Kinkade, C. W., Brinker, A., Zhang, R., Buckley, B., Brunner, J., Ohman‐Strickland, P., Qiu, X., Qasem, R. J., Fallon, J. K., et al. (2025) · Environmental Health Perspectives · doi:10.1289/ehp14478 · Cited by 7
Equality of specialist orthodontic care for adolescents in the Swedish public dental service: a cohort study
Göranson, E., Sonesson, M., Dimberg, L., Vähäsarja, N., & Naimi‐Akbar, A. (2025) · BMC Oral Health · doi:10.1186/s12903-025-06220-x · Cited by 7
“To guide the selection of variables for inclusion in the logistic regression analysis and adjustment for confounding, we constructed a Directed Acyclic Graph (DAG) for each of the primary exposure variables based on existing literature and subject-matter expertise [30, 31].”
Associations between long-term air pollution exposure and the development of amyotrophic lateral sclerosis: A matched case-control study
Saucier, D., Bélanger, M. F., Liu, Z., Lavigne, É., & O’Connell, C. M. (2025) · Environmental Research · doi:10.1016/j.envres.2025.122232 · Cited by 8
Causality and ability beliefs: An introduction to confounders and colliders
Al‐Hoorie, A. H., & Hiver, P. V. (2025) · Studies in Second Language Learning and Teaching · doi:10.14746/ssllt.48231 · Cited by 8
Maternal serum and placental metabolomes in association with prenatal exposure to per- and polyfluoroalkyl substances and their relevance to child neurodevelopment in an ASD-enriched cohort
Choi, J. W., Parenti, M., Slupsky, C. M., Tancredi, D. J., Schmidt, R. J., & Shin, H. (2025) · Environmental Pollution · doi:10.1016/j.envpol.2025.126811 · Cited by 8
A case-cohort study of the association between per- and polyfluoroalkyl substances (PFAS) and breast cancer among participants in the American Cancer Society’s Cancer Prevention Study-II
Shahi, S., Winquist, A. G., Troeschel, A. N., Diver, W. R., Hodge, J. M., Deubler, E. L., Patel, A. V., Newton, C. C., & Teras, L. R. (2025) · Environmental Research · doi:10.1016/j.envres.2025.122381 · Cited by 8
Mortality and 15-min green space access across urbanicity levels in China
Wang, W., Wang, F., Yang, C., Wang, J., Zhang, F., Liang, Z., Li, P., & Zhang, L. (2025) · Environmental Research · doi:10.1016/j.envres.2025.122929 · Cited by 8
Neuroimaging markers of dementia across race/ethnicity and sex/gender using an intersectional approach within the HABS‐HD cohort
Caunca, M. R., Bahorik, A. L., Jiang, X., Braskie, M. N., O’Bryant, S. E., & Yaffe, K. S. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70733 · Cited by 6
“To guide our covariate selection, we constructed a directed acyclic graph (DAG) and used Daggity online 27 (code available in Supple-mental Methods in supporting information) to determine the minimal set of covariates needed to estimate the total direct effect of both race/ethnicity and sex/gender…”
Addressing Selection and Confounding Biases in Dental Claims Data: A Causal Inference Framework for Periodontal–Systemic Disease Research
Wong, J. J., Urquhart, O., Carrasco‐Labra, A., Schisterman, E. F., & Glick, M. (2025) · Journal of Dental Research · doi:10.1177/00220345251387660 · Cited by 7
“DAGitty (www.dagitty.net) provides a web-based interface for drawing causal graphs and testing backdoor criteria, while the R package “dagitty” enables programmatic DAG analysis and integration with statistical workflows (Textor et al. 2016).”
Adherence to the planetary health diet and healthy aging: A prospective analysis
Maroto-Rodríguez, J., Ortolá, R., García‐Esquinas, E., Rodríguez-Artalejo, F., & Sotos‐Prieto, M. (2025) · Clinical Nutrition · doi:10.1016/j.clnu.2025.106562 · Cited by 7
Understanding the role of diabetes in the osteoarthritis disease and treatment process: a study protocol for the Swedish Osteoarthritis and Diabetes (SOAD) cohort
Dell’Isola, A., Vinblad, J., Lohmander, S. L., Svensson, J., Turkiewicz, A., Franzén, S., Nauclér, E., W‐Dahl, A., Abbott, A. D., Dahlberg, L., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-032923 · Cited by 7
Cancer Is an Independent Risk Factor for Acute Respiratory Distress Syndrome in Critically Ill Patients: A Single Center Retrospective Cohort Study
Ho, K., Gordon, J., Litzenberg, K. T., Exline, M. C., Englert, J. A., & Herman, D. D. (2021) · Journal of Intensive Care Medicine · doi:10.1177/08850666211005422 · Cited by 7
Maternal hemodynamics and neonatal birth weight in pregnancies complicated by gestational diabetes: new insights from novel causal inference analysis modeling
Anness, A. R., Clark, A., Melhuish, K., Leone, F. M. T., Osman, M. W., Webb, D. R., Robinson, T., Walkinshaw, N., Khalil, A. A., & Mousa, H. (2022) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.24864 · Cited by 7
Formal models for the study of the relationship between fluctuating asymmetry and fitness in humans
Farrera, A. (2022) · American Journal of Biological Anthropology · doi:10.1002/ajpa.24588 · Cited by 7
Cumulative effects of temperature on blood pressure during pregnancy: A cohort study of differing effects in three trimesters
Chen, Q., Wang, Y., Tang, H., Wang, Y., Gu, A., Zhai, X., & Zheng, M. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.160143 · Cited by 7
Prehospital vital sign monitoring in paediatric patients: an interregional study of educational interventions
Nielsen, V. M. L., Søvsø, M. B., Kløjgård, T. A., Skals, R. G., Corfield, A. R., Bender, L., Lossius, H. M., Mikkelsen, S. S., & Christensen, E. F. (2023) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/s13049-023-01067-z · Cited by 7
“We used DAGitty [29] to illustrate directed acyclic graphs for discussion of possible confounders for the primary outcome.”
Method
Effects of extended oral antibiotic prophylaxis on surgical site infections after instrumented spinal fusion: a cohort study of 901 patients with a minimum follow-up of 1 year
García, M. M., Otermin, I., Librero, J., Gutiérrez-Dubois, J., Cuevas, D. M., Cruz, J. I. A., Salvatierra, L. A., Diaz, I. A., & Ovejero, A. M. H. (2023) · Acta Orthopaedica · doi:10.2340/17453674.2023.9409 · Cited by 7
“net software to facilitate estimation of cause–effect relations, comparison of hypotheses, and statistical analysis (13,14).”
Method
Residential surrounding greenness is associated with improved lung function in adults: a cross-sectional study in eastern China
Zhang, W., Peng, W., Cai, J., jiang, Y., Zhou, C., Zha, Z., & Mi, J. (2023) · BMC Public Health · doi:10.1186/s12889-023-15473-6 · Cited by 7
“(2) We used directed acyclic graphs to identify which potential confounders to include in the statistical models (Figure S1) [38].”
Method
A Bayesian multilevel analysis exploring population-level effects mediating the relationship between area-level poverty and community-acquired Methicillin-resistant Staphylococcus aureus (CA-MRSA) infection across California communities
Bustamante, B. L. M., May, L. S., Fejerman, L., & Martínez‐López, B. (2023) · Health & Place · doi:10.1016/j.healthplace.2023.103094 · Cited by 7
Does exposure to nature make children more intelligent? Analysis in Polish children with and without ADHD
Buczyłowska, D., Baumbach, C., Bratkowski, J., Mysak, Y., Wierzba-Łukaszyk, M., Skotak, K., Sitnik‐Warchulska, K., Lipowska, M., Izydorczyk, B., Marcin Szwed, A., et al. (2023) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2023.114239 · Cited by 7
“A directed acyclic graph (DAG), as implemented in dagitty.net (Textor et al., 2016), was used to select a minimum su�cient set of confounders (Greenland et al., 1999) to avoid over-adjustment and to identify potential mediators.”
Method
Exogenous female sex steroid hormones and new-onset asthma in women: a matched case–control study
Zhang, G.-Q., Basna, R., Mathur, M. B., Lässer, C., Mincheva, R. K., Ekerljung, L., Wennergren, G., Rådinger, M., Lundbäck, B., Kankaanranta, H., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-03038-8 · Cited by 7
OSATS scoring confirms ICG enhancement of performance in laparoscopic radical gastrectomy: a post-hoc analysis of a randomized controlled trial
Huang, Z., He, Q.-C., Qiu, W., Ju, W., Zheng, C.-Y., Lin, G., Li, P., Wang, J.-B., Lin, J., Lü, J., et al. (2023) · International Journal of Surgery · doi:10.1097/js9.0000000000000830 · Cited by 7
“All DAGs were created and analyzed using the DAGitty 3.0 software [29] .”
MethodInfluential

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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