Causal Inference

Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed.[1][2] The science of why things occur is called etiology. Causal inference is an example of causal reasoning.

Inferring the cause of something has been described as: “…reason[ing] to the conclusion that something is, or is likely to be, the cause of something else”.[3]  “Identification of the cause or causes of a phenomenon, by establishing covariation of cause and effect, a time-order relationship with the cause preceding the effect, and the elimination of plausible alternative causes.”[4]

Epidemiological studies employ different epidemiological methods of collecting and measuring evidence of risk factors and effect and different ways of measuring association between the two. A hypothesis is formulated, and then tested with statistical methods (see Statistical hypothesis testing). It is statistical inference that helps decide if data are due to chance, also called random variation, or indeed correlated and if so how strongly. However, correlation does not imply causation, so further methods must be used to infer causation.

Epidemiology studies patterns of health and disease in defined populations of living beings in order to infer causes and effects. An association between an exposure to a putative risk factor and a disease may be suggestive of, but is not equivalent to causality because correlation does not imply causation. Historically, Koch’s postulateshave been used since the 19th century to decide if a microorganism was the cause of a disease. In the 20th century the Bradford Hill criteria, described in 1965[5] have been used to assess causality of variables outside microbiology, although even these criteria are not exclusive ways to determine causality.

In molecular epidemiology the phenomena studied are on a molecular biology level, including genetics, where biomarkers are evidence of cause or effects.

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  1. ^ Pearl, Judea (1 January 2009). “Causal inference in statistics: An overview”(PDF)Statistics Surveys3: 96–146. doi:10.1214/09-SS057.
  2. ^ Morgan, Stephen; Winship, Chris (2007). Counterfactuals and Causal inference. Cambridge University Press. ISBN 978-0-521-67193-4.
  3. ^ “causal inference”. Encyclopædia Britannica, Inc. Retrieved 24 August 2014.
  4. ^ John Shaughnessy; Eugene Zechmeister; Jeanne Zechmeister (2000). Research Methods in Psychology. McGraw-Hill Humanities/Social Sciences/Languages. pp. Chapter 1 : Introduction. ISBN 978-0077825362. Retrieved 24 August 2014.
  5. ^ Hill, Austin Bradford (1965). “The Environment and Disease: Association or Causation?”Proceedings of the Royal Society of Medicine58 (5): 295–300. PMC 1898525PMID 14283879.

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