Causal Diagrams for Epidemiologic Research
Abstract
Causal diagrams have a long history of informal use and, more recently, have undergone formal development for applications in expert systems and robotics. We provide an introduction to these developments and their use in epidemiologic research. Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates. They also provide a method for critical evaluation of traditional epidemiologic criteria for confounding. In particular, they reveal certain heretofore unnoticed shortcomings of those criteria when used in considering multiple potential confounders. We show how to modify the traditional criteria to correct those shortcomings. (Epidemiology 1999;10:37–48)
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SCM weeds out myths and misconceptions from outdated traditions.
“Weeding out myths and misconceptions from outdated traditions (Meek and Glymour, 1994; Greenland et al., 1999; Cole and Hern´ an, 2002; Arah, 2008; Shrier, 2009; Pearl, 2009b).”
From Causal inference in statistics: An overview (2009) · cites this paper as Greenland et al., 1999
SCM weeds out myths and misconceptions from outdated traditions.
“Weeding out myths and misconceptions from outdated traditions (Meek and Glymour, 1994, Greenland et al., 1999, Cole and Hernan, 2002, Arah, 2008, Shrier, 2009, Pearl, 2009c).”
From An Introduction to Causal Inference (2010) · cites this paper as Greenland et al., 1999
FA is one potential pathway linking the food environment to obesity, however there is a "backdoor path" through SES to STA, as well as a pathway that may not include FA.
“Taken together, FA is one potential pathway linking the food environment to obesity, however there is a “backdoor path” [100] through SES to STA, as well as a pathway that may not include FA (i.e., through the built environment).”
From Food Addiction and Psychosocial Adversity: Biological Embedding, Contextual Factors, and Public Health Implications (2020) · cites this paper as [100]
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Rodgers, K. M., Udesky, J. O., Rudel, R. A., & Brody, J. G. (2017) · Environmental Research · doi:10.1016/j.envres.2017.08.045 · Cited by 451 · see citing works
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Gebremedhin, A. T., Hogan, A. B., Blyth, C. C., Glass, K., & Moore, H. C. (2022) · Scientific Reports · doi:10.1038/s41598-021-04080-3 · Cited by 407 · see citing works
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Chaiton, M. O., Cohen, J. E., O'Loughlin, J., & Rehm, J. (2009) · BMC Public Health · doi:10.1186/1471-2458-9-356 · Cited by 391 · see citing works
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Brookhart, M. A., Patrick, A. R., Dormuth, C. R., Avorn, J., Shrank, W. H., Cadarette, S. M., & Solomon, D. H. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm070 · Cited by 386 · see citing works
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Xu, W., Collet, J., Shapiro, S. H., Lin, Y., Yang, T., Platt, R. W., Wang, C., & Bourbeau, J. (2008) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200804-619oc · Cited by 308 · see citing works
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Wolff, M. S., Teitelbaum, S. L., Pinney, S. M., Windham, G. C., Liao, L., Biro, F. M., Kushi, L. H., Erdmann, C., Hiatt, R. A., Rybak, M. E., et al. (2010) · Environmental Health Perspectives · doi:10.1289/ehp.0901690 · Cited by 301 · see citing works
Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L., van der Maas, H. L. J., & Maris, G. (2017) · Multivariate Behavioral Research · doi:10.1080/00273171.2017.1379379 · Cited by 290 · see citing works
Levine, K. B., Williams, R. E., & Hartmann, K. E. (2008) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0b013e31815a5168 · Cited by 295 · see citing works
Olsen, G., Butenhoff, J., & Zobel, L. R. (2009) · Reproductive Toxicology · doi:10.1016/j.reprotox.2009.02.001 · Cited by 296 · see citing works
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Bauer, G. R., & Scheim, A. I. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2018.12.015 · Cited by 299 · see citing works
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Greenland, S. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm355 · Cited by 281 · see citing works
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VanderWeele, T. J., & Shpitser, I. (2013) · The Annals of Statistics · doi:10.1214/12-aos1058 · Cited by 268 · see citing works
“M E ] 2 A pr 2 01 3 The Annals of Statistics 2013, Vol. 41, No. 1, 196–220 DOI: 10.1214/12-AOS1058 c© Institute of Mathematical Statistics, 2013 ON THE DEFINITION OF A CONFOUNDER1 By Tyler J. VanderWeele and Ilya Shpitser”
InfluentialMuir, S. W., Berg, K., Chesworth, B. M., Klar, N. S., & Speechley, M. R. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.06.010 · Cited by 275 · see citing works
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Han, H., Shehta, A., Ahn, S., et al. (2015) · Journal of Hepatology · doi:10.1016/j.jhep.2015.04.005 · Cited by 270 · see citing works
Bodnar, L. M., Krohn, M. A., & Simhan, H. N. (2009) · Journal of Nutrition · doi:10.3945/jn.108.103168 · Cited by 271 · see citing works
Kosek, M. (2017) · EBioMedicine · doi:10.1016/j.ebiom.2017.02.024 · Cited by 248 · see citing works
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Krishnan, S., Rocca, C. H., Hubbard, A., Subbiah, K., Edmeades, J., & PADIAN, N. S. (2009) · Social Science & Medicine · doi:10.1016/j.socscimed.2009.09.026 · Cited by 262 · see citing works
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Lebov, J., Grieger, K., Womack, D. S., Zaccaro, D. J., Whitehead, N. S., Kowalcyk, B. B., & MacDonald, P. D. M. (2017) · One Health · doi:10.1016/j.onehlt.2017.03.004 · Cited by 280 · see citing works
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Self-citationCausal inference with observational data: the need for triangulation of evidence
Hammerton, G., & Munafò, M. R. (2021) · Psychological Medicine · doi:10.1017/s0033291720005127 · Cited by 262 · see citing works
Liew, Z., Olsen, J., Cui, X., Ritz, B. R., & Arah, O. A. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyu249 · Cited by 257 · see citing works
- Authors
- Greenland, S., Pearl, J., & Robins, J. M.
- Affiliations
- Harvard University (US)
- Venue
- Epidemiology
- Published
- 1999-01
- Volume
- 10
- Issue
- 1
- Pages
- 37-48
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W4294553991
- Cited by
- 3,728
- References
- 0
- Citations by year
- 2026: 257 · 2025: 279 · 2024: 332 · 2023: 330 · 2022: 333 · 2021: 308 · 2020: 269 · 2019: 219 · 2018: 198 · 2017: 199 · 2016: 156 · 2015: 140 · 2014: 118 · 2013: 106 · 2012: 82
- FWCI
- 18.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- 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
- causal graphs, directed acyclic graphs, confounding, feature selection, causal inference, expert systems
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