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Causal thinking and causal language in epidemiology: a cause by any other name is still a cause: response to Lipton and Odegaard

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BioMed Central
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  • Biology
  • Communication
  • Medicine

Abstract

Tam, CC (2006) Causal thinking and causal language in epidemiol- ogy: a cause by any other name is still a cause: response to Lipton and Odegaard. Epidemiologic perspectives innovations , 3. p. 7. ISSN 1742-5573 Downloaded from: http://researchonline.lshtm.ac.uk/9863/ Usage Guidelines Please refer to usage guidelines at http://researchonline.lshtm.ac.uk/policies.html or alterna- tively contact [email protected] Available under license: Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ BioMed Central Page 1 of 4 (page number not for citation purposes) Epidemiologic Perspectives & Innovations Open AccessCommentary Causal thinking and causal language in epidemiology: a cause by any other name is still a cause: response to Lipton and Ødegaard Clarence C Tam* Address: Infectious Disease Epidemiology Unit, Department of Infectious & Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK Email: Clarence C Tam* - [email protected] * Corresponding author I have great sympathy with the thoughts of Lipton and Ødegaard [1] – the assessment and communication of "causal" associations is a source of continual frustration for epidemiologists. The authors' lucid account of the use of causal language in epidemiology can essentially (if rather unflatteringly) be simplified to the following: it is impossible to prove that X causes Y; the statement "Smok- ing causes lung cancer" is thus no more informative than the statement "Smoking two packs a day for N years increases your risk of lung cancer ten-fold". In fact, it is less informa- tive and even misleading. The authors argue that such causal statements are redundant, logically indefensible and should be avoided in favour of more detailed descrip- tions of the process by which such associations are estab- lished (the "story", as the authors put it). The latter are, in themselves, sufficient causal statements (the notion of "letting the data spe

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