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Causation and Causal Inference in Epidemiology
Open accessAbstract
Concepts of cause and causal inference are largely self-taught from early learning experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multicausality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes. Philosophers agree that causal propositions cannot be proved, and find flaws or practical limitations in all philosophies of causal inference. Hence, the role of logic, belief, and observation in evaluating causal propositions is not settled. Causal inference in epidemiology is better viewed as an exercise in measurement of an effect rather than as a criterion-guided process for deciding whether an effect is present or not.
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Cites
The Environment and Disease: Association or Causation?
Hill, A. B. (1965) · Proceedings of the Royal Society of Medicine · doi:10.1177/003591576505800503 · Cited by 6,974
ROTHMAN, K. J. (1976) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a112335 · Cited by 776
Self-citation
Cited by
Overadjustment bias and unnecessary adjustment in epidemiologic studies.
Schisterman, E. F., Cole, S. R., & Platt, R. W. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a819a1 · Cited by 2,096
“” One also finds reference to the term overadjustment in settings with unnecessary control for variables.(7) In summary, overadjustment sometimes means control (eg, by regression adjustment, stratification, restriction) for a variable that (a) increases rather than decreases net bias or (b) affects precision without a…”
BackgroundThe Lancet Commission on diabetes: using data to transform diabetes care and patient lives
Chan, J. C. N., Lim, L.-L., Wareham, N. J., Shaw, J. E., Orchard, T. J., Zhang, P., Lau, E. S. H., Eliasson, B., Kong, A. P. S., Ezzati, M., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)32374-6 · Cited by 793
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.04615 · Cited by 569
Diet and Dental Caries: The Pivotal Role of Free Sugars Reemphasized
Sheiham, A., & James, W. P. T. (2015) · Journal of Dental Research · doi:10.1177/0022034515590377 · Cited by 520
A review of current evidence for the causal impact of attentional bias on fear and anxiety
Van Bockstaele, B., Verschuere, B., Tibboel, H., De Houwer, J., Crombez, G., & Koster, E. H. W. (2014) · Psychological Bulletin · doi:10.1037/a0034834 · Cited by 485
Beebe, D. W. (2006) · Sleep · doi:10.1093/sleep/29.9.1115 · Cited by 446
Experimental Designs for Identifying Causal Mechanisms
Imai, K., Tingley, D., & Yamamoto, T. (2013) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2012.01032.x · Cited by 408
Stolk, R. P., Rosmalen, J. G. M., Postma, D. S., de Boer, R. A., Navis, G., Slaets, J. P. J., Ormel, J., & Wolffenbuttel, B. H. R. (2008) · European Journal of Epidemiology · doi:10.1007/s10654-007-9204-4 · Cited by 391
Broadening the range of designs and methods for impact evaluations
Stern, E., Stame, N., Mayne, J., Forss, K., Davies, R., & Befani, B. (2012) · doi:10.22163/fteval.2012.100 · Cited by 369
Prevalence of chronic pain in a representative sample in the United States.
Hardt, J., Jacobsen, C., Goldberg, J., Nickel, R., & Buchwald, D. (2008) · Pain Medicine · doi:10.1111/j.1526-4637.2008.00425.x · Cited by 324
Nurse staffing levels and patient outcomes: a systematic review of longitudinal studies.
Dall'Ora, C., Saville, C., Rubbo, B., Turner, L., Jones, J., & Griffiths, P. (2022) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2022.104311 · Cited by 314
Observational research--opportunities and limitations
Boyko, E. J. (2013) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2013.07.007 · Cited by 313
Hidden wounds? inflammatory links between childhood trauma and psychopathology
Danese, A., & Baldwin, J. R. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-010416-044208 · Cited by 286
Gene–Environment Interactions in Severe Mental Illness
Uher, R. (2014) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2014.00048 · Cited by 251
False Alarms and Pseudo-Epidemics: The Limitations of Observational Epidemiology
Grimes, D. A., & Schulz, K. F. (2012) · Obstetrics & Gynecology · doi:10.1097/aog.0b013e31826af61a · Cited by 254
Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals
Dahabreh, I. J., & Bibbins-Domingo, K. (2024) · JAMA · doi:10.1001/jama.2024.7741 · Cited by 242
Is meta-analysis the platinum standard of evidence?
Stegenga, J. (2011) · Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences · doi:10.1016/j.shpsc.2011.07.003 · Cited by 193
Formalizing the role of agent-based modeling in causal inference and epidemiology
Marshall, B. D. L., & Galea, S. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu274 · Cited by 192
Critical review on PFOA, kidney cancer, and testicular cancer
Bartell, S. M., & Vieira, V. M. (2021) · Journal of the Air & Waste Management Association · doi:10.1080/10962247.2021.1909668 · Cited by 151
From Many to One to Many—the Search for Causes of Psychiatric Illness
Kendler, K. S. (2019) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.1200 · Cited by 129
Epigenetic Research in Neuropsychiatric Disorders: the “Tissue Issue”
Bakulski, K. M., Halladay, A., Hu, V. W., Mill, J., & Fallin, M. D. (2016) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-016-0083-4 · Cited by 127
The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"
Ward, A. C. (2009) · Epidemiologic Perspectives & Innovations · doi:10.1186/1742-5573-6-2 · Cited by 120
“claim, there are ambiguities, fallacies and vagaries in each of the Bradford Hill criteria [99,100].”
Campeny, E., López-Pelayo, H., Nutt, D., Blithikioti, C., Oliveras, C., Nuño, L., Maldonado, R., Florez, G., Arias, F., Fernández-Artamendi, S., et al. (2020) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2020.02.003 · Cited by 127
Adaptation to chronic ER stress enforces pancreatic β-cell plasticity
Chen, C.-W., Guan, B.-J., Alzahrani, M. R., Gao, Z., Gao, L., Bracey, S., Wu, J., Mbow, C. A., Jobava, R., Haataja, L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32425-7 · Cited by 116
Fornaro, M., Anastasia, A., Novello, S., Fusco, A., Solmi, M., Monaco, F., Veronese, N., De Berardis, D., & de Bartolomeis, A. (2018) · Bipolar Disorders · doi:10.1111/bdi.12612 · Cited by 93
Dragovic, M., Pascu, V., Hall, T., Ingram, J., & Waters, F. (2020) · Australasian Psychiatry · doi:10.1177/1039856220960673 · Cited by 80
Vitamin D and the evolution of human depigmentation
Chaplin, G., & Jablonski, N. G. (2009) · American Journal of Physical Anthropology · doi:10.1002/ajpa.21079 · Cited by 87
Randomized Trials vs Real-world Evidence: How Can Both Inform Decision-making?
Sheldrick, R. C. (2023) · JAMA · doi:10.1001/jama.2023.4855 · Cited by 88
Insulin resistance and the metabolic syndrome—or the pitfalls of epidemiology
Yudkin, J. S. (2007) · Diabetologia · doi:10.1007/s00125-007-0711-3 · Cited by 75
Steenland, K., Schubauer-Berigan, M. K., Vermeulen, R., Lunn, R. M., Straif, K., Zahm, S., Stewart, P., Arroyave, W. D., Mehta, S. S., & Pearce, N. (2020) · Environmental Health Perspectives · doi:10.1289/ehp6980 · Cited by 69
Gans, R. O. B. (2006) · Medical Clinics of North America · doi:10.1016/j.mcna.2006.05.002 · Cited by 64
Understanding long COVID: nosology, social attitudes and stigma
Byrne, E. A. (2022) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2021.09.012 · Cited by 61
The use of genetic epidemiology to guide classification in child and adult psychopathology
Szatmari, P., White, J., & Merikangas, K. R. (2007) · International Review of Psychiatry · doi:10.1080/09540260701563619 · Cited by 38
The study of gene environment interactions in psychiatry: Limited gains at a substantial cost?
Zammit, S., Wiles, N., & Lewis, G. (2010) · Psychological Medicine · doi:10.1017/s0033291709991280 · Cited by 25
Genser, B., Teles, C. A., Barreto, M. L., & Fischer, J. E. (2015) · Environmental Health · doi:10.1186/s12940-015-0047-2 · Cited by 23
Mental Health Effects of Changes in Psychosocial Work Characteristics: A Prospective Cohort Study
De Raeve, L., Vasse, R. M., Jansen, N. W. H., van den Brandt, P. A., & Kant, I. (2007) · Journal of Occupational & Environmental Medicine · doi:10.1097/jom.0b013e31811eadd3 · Cited by 16
Can We Establish Causality with Statistical Analyses? The Example of Epidemiology
Frick, U., & Rehm, J. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118947074.ch17 · Cited by 14
The Precautionary Principle Meets the Hill Criteria of Causation
Steel, D., & Yu, J. (2019) · Ethics, Policy & Environment · doi:10.1080/21550085.2019.1581420 · Cited by 4
Metadata
- Authors
- Rothman, K. J., & Greenland, S.
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 2005-07
- Volume
- 95
- Issue
- S1
- Pages
- S144-S150
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- OpenAlex
- W2096570124
- Cited by
- 1,259
- References
- 2
- FWCI
- 33.39
- Type
- journal article
- Language
- en
- Open access
- green
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