The C-word: Scientific euphemisms do not improve causal inference from observational data

American Journal of Public Health

Volumn 108, Issue 5, 2018, Pages 616-619

  Hernán, Miguel A ^a  

^a HARVARD MIT DIVISION OF HEALTH SCIENCES AND TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA ANALYSIS; ERROR; HUMAN; NOTE; CAUSALITY; MEDICAL RESEARCH; OBSERVATIONAL STUDY;

BIOMEDICAL RESEARCH; CAUSALITY; HUMANS; OBSERVATIONAL STUDIES AS TOPIC;

EID: 85045006911     PISSN: 00900036     EISSN: 15410048     Source Type: Journal    
DOI: 10.2105/AJPH.2018.304337     Document Type: Note

References (19)

1. Ruich P. The use of cause-and-effect language in the JAMA Network journals. AMA Style Insider. 2017. Available at: http://amastyleinsider.com/2017/09/19/use-cause-effect-language-jama-network-journals. Accessed January 14, 2018.
2. Savitz DA. Re: “Associations are not effects.” Am J Epidemiol. 1991;134(4): 442-444.
3. Rothman KJ. Modern Epidemiology. Boston, MA: Little, Brown and Company; 1986.
4. Hernán MA. Does water kill? A call for less casual causal inferences. Ann Epidemiol. 2016;26(10):674-680.
5. Kaufman JS. There is no virtue in vagueness. Ann Epidemiol. 2016;26(10): 683-684.
6. Robins JM, Weissman M. Counter-factual causation and streetlamps. What is to be done? Int J Epidemiol. 2016;45(6): 1830-1835.
7. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758-764.
8. Robins JM, Greenland S. The role of model selection in causal inference from nonexperimental data. Am J Epidemiol. 1986;123(3):392-402.
9. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5): 615-625.
10. Hernán MA, Hernández-Díaz S, Werler MM, Mitchell AA. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol. 2002;155(2):176-184.
11. Greenland S, Pearl J. Adjustments and their consequences-collapsibility analysis using graphical models. Int Stat Rev. 2011; 79(3):401-426.
12. Greenland S, Neutra R. Control of confounding in the assessment of medical technology. Int J Epidemiol. 1980;9(4): 361-367.
13. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48.
14. Greenland S, Pearl J, Robins JM. Confounding and collapsibility in causal inference. Stat Sci. 1999;14(1):29-46.
15. Hernández-Díaz S, Schisterman EF, Hernán MA. The birth weight “paradox” uncovered? Am J Epidemiol. 2006;164(11): 1115-1120.
16. Hernán MA, Clayton D, Keiding N. The Simpson’s paradox unraveled. Int J Epidemiol. 2011;40(3):780-785.
17. Snoep JD, Morabia A, Hernández-Díaz S, Hernán MA, Vandenbroucke JP. Commentary: a structural approach to Berkson’s fallacy and a guide to a history of opinions about it. Int J Epidemiol. 2014; 43(2):515-521.
18. Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol. 2013;177(4):292-298.
19. Pearl J. Causality: Models, Reasoning, and Inference. 2nd ed. New York, NY: Cambridge University Press; 2009.