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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Method
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Paulin, T. K., Malmgren, L., McGuigan, F. E., & Åkesson, K. (2024) · Calcified Tissue International · doi:10.1007/s00223-023-01181-1 · Cited by 30
“The variables adjusted for were determined using Directed Acyclic Graph (DAGs) [31].”
Method
Personality Neuroscience
DeYoung, C. G., & Blain, S. D. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108264822.026 · Cited by 34
SEPLINE: Socioeconomic Position in Epidemiological Research—A National Guideline on Danish Registry Data
Hjorth, C. F., Kjærulff, T. M., Thomsen, M. K., Cronin-Fenton, D. P., Dalton, S. O., & Olsen, M. H. (2025) · Clinical Epidemiology · doi:10.2147/clep.s520772 · Cited by 28
“78 Moreover, tools such as Directed Acyclic Graphs 79 or causal loop diagrams 80 can be used to visualize underlying theoretical assumptions.”
Lifestyle factors and visceral adipose tissue: Results from the PREDIMED-PLUS study
Galmes-Panades, A. M., Konieczna, J., Abete, I., Colom, A., Rosique-Esteban, N., Zulet, M. Á., Ruiz, Z. V., Estruch, R., Vidal, J., Toledo, E., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210726 · Cited by 29
Right single lung transplantation or double lung transplantation compared with left single lung transplantation in chronic obstructive pulmonary disease
Benvenuto, L. J., Costa, J. A., Piloni, D., Aversa, M. M., Anderson, M. R., Shah, L., Robbins, H. Y., Stanifer, B. P., Sonett, J. R., Arcasoy, S. M., et al. (2020) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2020.06.009 · Cited by 28
Orthogeriatric co-management reduces incidence of delirium in hip fracture patients
Pollmann, C. T., Mellingsæter, M. R., Neerland, B. E., Straume-Næsheim, T. M., Årøen, A., & Watne, L. O. (2021) · Osteoporosis International · doi:10.1007/s00198-021-05974-8 · Cited by 28
Gastrointestinal parasites in Swedish pigs: Prevalence and associated risk factors for infection in herds where animal welfare standards are improved
Pettersson, E., Sjölund, M., Dórea, F. C., Lind, E. O., Grandi, G., Jacobson, M., Höglund, J., & Wallgren, P. (2021) · Veterinary Parasitology · doi:10.1016/j.vetpar.2021.109459 · Cited by 28
Sex-specific effects of prenatal organophosphate ester (OPE) metabolite mixtures and adverse infant birth outcomes in the maternal and developmental risks from environmental and social stressors (MADRES) pregnancy cohort
Hernandez‐Castro, I., Eckel, S. P., Howe, C. G., Niu, Z., Kannan, K., Robinson, M., Foley, H. B., Grubbs, B. H., Al-Marayati, L. A., Lerner, D. L., et al. (2023) · Environmental Research · doi:10.1016/j.envres.2023.115703 · Cited by 28
Nut consumption in a representative survey of Australians: a secondary analysis of the 2011–2012 National Nutrition and Physical Activity Survey
Nikodijevic, C. J., Probst, Y., Batterham, M. J., Tapsell, L. C., & Neale, E. P. (2020) · Public Health Nutrition · doi:10.1017/s1368980019004117 · Cited by 28
“To explore causality in these models, directed acyclic graphs were developed using DAGitty(24).”
Method
The impact of gestational weight gain on fetal and neonatal outcomes: the Araraquara Cohort Study
Victor, A., Teles, L. D. F. D. S., Aires, I. O., de Carvalho, L. F., Luzia, L. A., Artes, R., & Rondó, P. H. D. C. (2024) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-024-06523-x · Cited by 27
“The DAG was constructed using DAGitty software (version 3.0; Nijmegen, GE, Netherlands) [21] Graphical criteria were applied for the selection of adjusted covariates, aiming to minimize bias in variable selection [22, 23].”
MethodInfluential
Sleep problems mediate the association between outdoor nighttime light and symptoms of depression and anxiety: A cross-sectional, multi-city study in Bulgaria
Helbich, M., Burov, A., Dimitrova, D., Markevych, I., Nieuwenhuijsen, M. J., & Dzhambov, A. M. (2024) · Environmental Research · doi:10.1016/j.envres.2024.119897 · Cited by 28
Alcohol intake and the risk of intracerebral hemorrhage in the elderly
Costa, P., Grassi, M., Iacoviello, L., Zedde, M., Marcheselli, S., Silvestrelli, G., DeLodovici, M. L., Sessa, M., Zini, A., Paciaroni, M., et al. (2018) · Neurology · doi:10.1212/wnl.0000000000005814 · Cited by 27
Relationship between weight status and cognition in children: A mediation analysis of physical fitness components
Hermosa, A. R., Mota, J. A. P. S., Díez‐Fernández, A., Martínez‐Vizcaíno, V., Redondo‐Tébar, A., & Sánchez‐López, M. (2019) · Journal of Sports Sciences · doi:10.1080/02640414.2019.1676538 · Cited by 29
Consumption of sugar‐sweetened and artificially sweetened soft drinks and risk of cancers not related to obesity
Bassett, J. K., Milne, R. L., English, D. R., Giles, G. G., & Hodge, A. M. (2019) · International Journal of Cancer · doi:10.1002/ijc.32772 · Cited by 29
Associations between self-reported sleep characteristics and incident mild cognitive impairment: The Heinz Nixdorf Recall Cohort Study
Brachem, C., Winkler, A., Tebrügge, S., Weimar, C., Erbel, R., Jöckel, K., Stang, A., Dragano, N., Moebus, S., Kowall, B., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-63511-9 · Cited by 31
Within-City Variation in Reactive Oxygen Species from Fine Particle Air Pollution and COVID-19
Stieb, D. M., Evans, G. J., To, T. M., Lakey, P. S. J., Shiraiwa, M., Hatzopoulou, M., Minet, L., Brook, J. R., Burnett, R. T., & Weichenthal, S. (2021) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202011-4142oc · Cited by 27
“Owing to the relatively small sample size,wewerenotabletoincludealargenumber of covariates, opting instead to base themodel on a DAG.”
MethodInfluential
Attention-deficit/hyperactivity disorder has a state-dependent association with asthma: The role of systemic inflammation in a population-based birth cohort followed from childhood to adulthood
Leffa, D. T., Caye, A., Santos, I. S., Matijasevich, A., Menezes, A. M. B., Wehrmeister, F. C., Oliveira, I. O., Vitola, E. S., Bau, C. H. D., Grevet, E. H., et al. (2021) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2021.08.004 · Cited by 27
“Residential greenness and site-specific cancer: A registry based cohort of 144,427 participants with a 21-years of follow-up, Tel-Aviv district, Israel”
Kayyal-Tarabeia, I., Michael, Y., Lensky, I. M., Blank, M., & Agay‐Shay, K. (2022) · Environmental Research · doi:10.1016/j.envres.2022.113460 · Cited by 27
Influence of maternal age on birth and infant outcomes at 6 months: a cohort study with quantitative bias analysis
Gebreegziabher, E., Bountogo, M., Sié, A., Zakané, A., Compaoré, G., Ouédraogo, T., Lebas, E., Nyatigo, F., Glymour, M. M., Arnold, B. F., et al. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac236 · Cited by 32
“Potential confounders of the association between maternal age and birth and growth outcomes and mortality were selected a priori using a directed acyclic graph (DAG) 19 (Supplementary Figure 1, available as Supplementary data at IJE online) and based on previous literature on this topic.”
Mediating effect of endocrine hormones on association between per- and polyfluoroalkyl substances exposure and birth size: Findings from sheyang mini birth cohort study
Wang, Z., Zhang, J., Dai, Y., Zhang, L., Guo, J., Xu, S., Chang, X., Wu, C., & Zhou, Z. (2023) · Environmental Research · doi:10.1016/j.envres.2023.115658 · Cited by 27
Neurodevelopment and healthcare utilisation at age 5–6 years in bronchopulmonary dysplasia: an EPIPAGE-2 cohort study
Tréluyer, L., Nuytten, A., Guellec, I., Jarreau, P., Benhammou, V., Cambonie, G., Truffert, P., Marchand‐Martin, L., Ancel, P., & Torchin, H. (2023) · Archives of Disease in Childhood Fetal & Neonatal · doi:10.1136/archdischild-2023-325376 · Cited by 27
Association of workplace bullying and burnout with nurses’ suicidal ideation in Bangladesh
Kabir, H., Chowdhury, S. R., Roy, A. K., Chowdhury, S. A., Islam, M. N., Chomon, R. J., Akter, M., & Hossain, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-41594-4 · Cited by 30
Effect of Smoking on Breast Cancer by Adjusting for Smoking Misclassification Bias and Confounders Using a Probabilistic Bias Analysis Method
Pakzad, R., Nedjat, S., Yaseri, M., Salehiniya, H., Mansournia, N., Nazemipour, M., & Mansournia, M. A. (2020) · Clinical Epidemiology · doi:10.2147/clep.s252025 · Cited by 27
Adults with metabolically healthy overweight or obesity present more brown adipose tissue and higher thermogenesis than their metabolically unhealthy counterparts
Jurado‐Fasoli, L., Sánchez‐Delgado, G., Alcántara, J. M. A., Acosta, F. M., Sánchez, R. S., Labayen, I., Ortega, F. B., Martínez‐Téllez, B., & Ruiz, J. R. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2023.104948 · Cited by 26
“Then, we used the online tool DAGitty 52 to build a directed acyclic graph (DAG), 53 and the covariates were consequently selected (Figure S1).”
Method
The role of childhood cumulative trauma in the risk of lifetime PTSD: An epidemiological study
da Silva, H. C., Vilete, L. M. P., Coutinho, E. S. F., Luz, M. P., Mendlowicz, M. V., Portela, C. M., Figueira, I. L. D. V., Ventura, P. R., Mari, J. D. J., Quintana, M. I., et al. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.115887 · Cited by 36
Impact of DMARD treatment and systemic inflammation on all-cause mortality in patients with rheumatoid arthritis and interstitial lung disease: a cohort study from the German RABBIT register
Rudi, T., Zietemann, V., Meißner, Y., Zink, A., Krause, A., Lorenz, H., Kneitz, C., Schaefer, M., & Strangfeld, A. (2024) · RMD Open · doi:10.1136/rmdopen-2023-003789 · Cited by 26
The Association of Nighttime Fasting Duration and Prostate Cancer Risk: Results from the Multicase-Control (MCC) Study in Spain
Cros, A. P., Espinosa, A., Straíf, K., Pérez‐Gómez, B., Papantoniou, K., Gómez‐Acebo, I., Molina‐Barceló, A., Olmedo‐Requena, R., Alguacil, J., Fernández‐Tardón, G., et al. (2021) · Nutrients · doi:10.3390/nu13082662 · Cited by 33
“First, on the basis of the limited prior knowledge, we built a directed acyclic graph using Daggity (Figure S1) [28].”
Method
In utero exposure to parabens and early childhood BMI z-scores – Associations between placental ethyl paraben, longitudinal BMI trajectories and cord blood metabolic biomarkers
Reimann, B., Vrijens, K., Roels, H. A., Wang, C., Cosemans, C., Overmeire, I. V., Nawrot, T. S., & Plusquin, M. (2021) · Environment International · doi:10.1016/j.envint.2021.106845 · Cited by 26
“S2), which was built with the freely available online tool DAGitty 3.0 (Textor et al., 2016), to select a priori potential confounders as covariates.”
MethodInfluential
Outcomes and prognostic factors of decompensated pulmonary hypertension in the intensive care unit
Garcia, M. V. F., Souza, R., Costa, E. L. V., Fernandes, C. J. C. D. S., Jardim, C., & Caruso, P. (2021) · Respiratory Medicine · doi:10.1016/j.rmed.2021.106685 · Cited by 26
The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS)
Lowe, M. E., Akhtari, F. S., Potter, T. A., Fargo, D. C., Schmitt, C., Schurman, S. H., Eccles, K. M., Motsinger‐Reif, A. A., Hall, J. E., & Messier, K. P. (2022) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-022-00502-0 · Cited by 27
“We used a directed acyclic graph (DAG) to show covariates in relationships with an approach modeled after a causal inference framework; however, there are assumptions that our dataset does not meet for inference of causal estimates [30].”
Background
Effects of Sociodemographic Characteristics, Comorbidity, and Coexposures on the Association between Air Pollution and Type 2 Diabetes: A Nationwide Cohort Study
Sørensen, M., Poulsen, A. H., Hvidtfeldt, U. A., Christensen, J. H., Brandt, J. G., Frohn, L. M., Ketzel, M., Andersen, C., Valencia, V. H., Lassen, C. F., et al. (2023) · Environmental Health Perspectives · doi:10.1289/ehp11347 · Cited by 26
“…area-level SES variables, selected based on availability and ﬁ ndings of previous papers showing associations between the SES variables and the outcome of interest (T2D) as well as exposure to air pollution (see Directed Acyclic Graph in Figure S1 generated in DAGitty, version 3.0 44 ).”
Influential
Plant-based diets and the risk of pancreatic cancer: a large prospective multicenter study
Zhong, G., Li, Z., You, A.-J., Zhu, Q., Wang, C., & Yang, P.-F. (2023) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2022.11.013 · Cited by 29
Causalvis: Visualizations for Causal Inference
Guo, G., Karavani, E., Endert, A., & Kwon, B. C. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581236 · Cited by 36
“Traditionally, these causal relationships would be manually specified by a domain expert, such as a doctor or physician, aided by tools like Dagitty [58] that enable interactive specification of DAGs.”
Method
Emotional and informational social support from health visitors and breastfeeding outcomes in the UK
Chambers, A. R., Emmott, E. H., Myers, S., & Page, A. E. (2023) · International Breastfeeding Journal · doi:10.1186/s13006-023-00551-7 · Cited by 31
“DAGs (Directed Acyclic Graphs) We take a directed acyclic graph (DAG) approach to model selection [44] using the R package dagitty [45], with separate DAGs for the duration and experience analysis used to identify the minimally sufficient adjustment set of variables to control for.”
Method
Medication Nonadherence and Risk of Violence to Others Among Patients With Schizophrenia in Western China
Li, Y., Wen, H., Xiong, C., Lin, C., Yang, X., Wang, D., Fan, R., Liu, J., Zhao, X., Liu, Y., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.5891 · Cited by 26
The Impact of Physical Activity Intensity on the Dynamic Progression of Cardiometabolic Multimorbidity: Prospective Cohort Study Using UK Biobank Data
Liu, B.-P., Zhu, J.-H., Wan, L.-P., Zhao, Z., Wang, X., & Jia, C.-X. (2023) · JMIR Public Health and Surveillance · doi:10.2196/46991 · Cited by 25
“According to previous studies [38,39], an a priori–defined directed acyclic graph [40] was drawn to show the relationship between the covariates and outcomes (Figure S2 in Multimedia Appendix 1).”
Method
Association of Depression with Uncontrolled Hypertension in Primary Care Setting: A Cross‐Sectional Study in Less‐Developed Northwest China
Wang, L., Li, N., Heizhati, M., Li, M., Yang, Z., Wang, Z., & Abudereyimu, R. (2021) · International Journal of Hypertension · doi:10.1155/2021/6652228 · Cited by 25
“See the DAGitty user manual for detailed working principles and operation methods [32].”
Method
Incidence, clinical patterns, and risk factors of postpartum uterine diseases in dairy cows from high-altitude tropical herds
Timarán, D. A. V., Reyes-Vélez, J., Gilbert, R. O., Lefebvre, R. C., Palacio, L. G., & Maldonado-Estrada, J. G. (2021) · Journal of Dairy Science · doi:10.3168/jds.2020-18692 · Cited by 25
Association of Lumbar Spine Radiographic Changes With Severity of Back Pain–Related Disability Among Middle-aged, Community-Dwelling Women
Chen, L., Perera, R. S., Radojčić, M. R., Beckenkamp, P. R., Ferreira, P. H., Hart, D. J., Spector, T. D., Arden, N., & Ferreira, M. L. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.10715 · Cited by 25
ABO‐incompatible kidney transplantation in perspective of deceased donor transplantation and induction strategies: a propensity‐matched analysis
de Weerd, A. E., van den Brand, J. A. J. G., Bouwsma, H., de Vries, A. P. J., Dooper, I. M. M., Sanders, J., Christiaans, M. H. L., van Reekum, F. E., van Zuilen, A. D., Bemelman, F. J., et al. (2021) · Transplant International · doi:10.1111/tri.14145 · Cited by 26
Complications Post-COVID-19 and Risk Factors among Patients after Six Months of a SARS-CoV-2 Infection: A Population-Based Prospective Cohort Study
Domènech-Montoliu, S., Puig‐Barberà, J., Pac-Sa, M. R., Vidal-Utrillas, P., Latorre-Poveda, M., Rio-González, A. D., Ferrando-Rubert, S., Ferrer-Abad, G., Sanchéz-Urbano, M., Aparisi-Esteve, L., et al. (2022) · Epidemiologia · doi:10.3390/epidemiologia3010006 · Cited by 25
“0 version (Johannes Textor, Nijmegen, The Netherlands) [36,37] to define the potential confounders of each independent variable.”
BackgroundInfluential
Glycated haemoglobin and the risk of postoperative complications in people without diabetes: a prospective population‐based study in UK Biobank
Lam, S., Kumar, B., Loke, Y. K., Orme, S. E., & Dhatariya, K. (2022) · Anaesthesia · doi:10.1111/anae.15684 · Cited by 25
“We calculated an age- and sex-adjusted model as well as total effects and direct effects models based on a directed acyclic graph [13] (Fig.”
Method
Past mercury exposure and current symptoms of nervous system dysfunction in adults of a First Nation community (Canada)
Philibert, A., Fillion, M., Silva, J. D., Lena, T. S., & Mergler, D. (2022) · Environmental Health · doi:10.1186/s12940-022-00838-y · Cited by 25
“Modifications to DAGimplied adjustment sets include conditioning confounding and variables with ambiguous causing roles [55, 56].”
Background

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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