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A definition of causal effect for epidemiological research
Open accessAbstract
Estimating the causal effect of some exposure on some outcome is the goal of many epidemiological studies. This article reviews a formal definition of causal effect for such studies. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a discussion of sampling variability and a generalisation of this causal theory. The difference between association and causation is described—the redundant expression “causal effect” is used throughout the article to avoid confusion with a common use of “effect” meaning simply statistical association—and shows why, in theory, randomisation allows the estimation of causal effects without further assumptions. The article concludes with a discussion on the limitations of randomised studies. These limitations are the reason why methods for causal inference from observational data are needed.
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Cites
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
Robins, J. (1986) · Mathematical Modelling · doi:10.1016/0270-0255(86)90088-6 · Cited by 2,451
Stalnaker, R. C. (1968) · IFS · doi:10.1007/978-94-009-9117-0_2 · Cited by 2,220
An Enquiry concerning Human Understanding
Hume, D. (2008) · Oxford University Press eBooks · doi:10.1093/owc/9780199549900.001.0001 · Cited by 1,774
On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Splawa-Neyman, J., Dabrowska, D. M., & Speed, T. P. (1990) · Statistical Science · doi:10.1214/ss/1177012031 · Cited by 1,627
Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment
Rubin, D. B. (1980) · Journal of the American Statistical Association · doi:10.2307/2287653 · Cited by 1,368
(1958) · Medical Entomology and Zoology · Cited by 1,059
Pringle-Pattison, A. S., & Dewey, J. (1904) · The Philosophical Review · doi:10.2307/2176309 · Cited by 540
Robins, J. M. (1987) · Computers & Mathematics with Applications · doi:10.1016/0898-1221(87)90237-9 · Cited by 91
Cited by
Understanding and misunderstanding randomized controlled trials
Deaton, A., & Cartwright, N. (2018) · Social Science & Medicine · doi:10.1016/j.socscimed.2017.12.005 · Cited by 1,841
Odds Ratios for Mediation Analysis for a Dichotomous Outcome
VanderWeele, T. J., & Vansteelandt, S. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq332 · Cited by 809
Marginal Structural Models for the Estimation of Direct and Indirect Effects
VanderWeele, T. J. (2009) · Epidemiology · doi:10.1097/ede.0b013e31818f69ce · Cited by 583
Causal Inference and Observational Research: The Utility of Twins
McGue, M., Osler, M., & Christensen, K. (2010) · Perspectives on Psychological Science · doi:10.1177/1745691610383511 · Cited by 529
Snowden, J. M., Rose, S., & Mortimer, K. M. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwq472 · Cited by 511
Metadata
- Authors
- Hernán, M. A.
- Venue
- Journal of Epidemiology and Community Health
- Publisher
- BMJ
- Published
- 2004-03-16
- Volume
- 58
- Issue
- 4
- Pages
- 265-271
- ISSN
- 0141-7681 (ISSN-L)
- 0143-005X
- 1470-2738
- OpenAlex
- W2150300473
- Cited by
- 462
- References
- 12
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- bronze
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