Editing Epidemiology (section)

== Causal inference ==
{{main|Causal inference}}
Although epidemiology is sometimes viewed as a collection of statistical tools used to elucidate the associations of exposures to health outcomes, a deeper understanding of this science is that of discovering ''causal'' relationships.

"[[Correlation does not imply causation]]" is a common theme for much of the epidemiological literature. For epidemiologists, the key is in the term [[inference]]. Correlation, or at least association between two variables, is a necessary but not sufficient criterion for the inference that one variable causes the other. Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal.

Epidemiologists emphasize that the "'''one cause – one effect'''" understanding is a simplistic mis-belief.<ref>{{cite journal |last=Woodward |first=James |date=2010 |title=Causation in biology: stability, specificity, and the choice of levels of explanation. |journal=Biology & Philosophy |volume=25 |issue=3 |pages=287–318 |doi=10.1007/s10539-010-9200-z |s2cid=42625229 |url=http://philsci-archive.pitt.edu/4813/1/09.doc |via=SpringerLink}}</ref> Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes.<ref>{{cite book|title=Modern Epidemiology|last=Rothman|first=Kenneth J.|publisher=Little, Brown and Company|year=1986|isbn=978-0-316-75776-8|location=Boston/Toronto|url-access=registration|url=https://archive.org/details/modernepidemiolo0000roth}}</ref> Causes can be distinguished as necessary, sufficient or probabilistic conditions. If a necessary condition can be identified and controlled (e.g., antibodies to a disease agent, energy in an injury), the harmful outcome can be avoided (Robertson, 2015). One tool regularly used to conceptualize the multicausality associated with disease is the [[causal pie model]].<ref>{{cite book|last=Rothman|first=Kenneth J.|url=https://www.worldcat.org/oclc/750986180|title=Epidemiology : An introduction|date=2012|publisher=Oxford University Press|isbn=978-0-19-975455-7|edition=2nd|location=New York, NY|pages=24|oclc=750986180}}</ref>

=== Bradford Hill criteria ===
{{Main|Bradford Hill criteria}}
In 1965, [[Austin Bradford Hill]] proposed a series of considerations to help assess evidence of causation,<ref name="bh65">{{cite journal |last=Hill |first=Austin Bradford |year=1965 |title=The Environment and Disease: Association or Causation? |journal=[[Proceedings of the Royal Society of Medicine]] |volume=58 |pages=295–300 |url=http://www.edwardtufte.com/tufte/hill |pmid=14283879 |pmc=1898525 |issue=5 |doi=10.1177/003591576505800503}}</ref> which have come to be commonly known as the "[[Bradford Hill criteria]]". In contrast to the explicit intentions of their author, Hill's considerations are now sometimes taught as a checklist to be implemented for assessing causality.<ref>{{cite journal |last1=Phillips |first1=Carl V. |author2=Karen J. Goodman |title=The missed lessons of Sir Austin Bradford Hill |journal=Epidemiologic Perspectives & Innovations |volume=1 |issue=3 |date=October 2004 |pmid=15507128 |pmc=524370 |doi=10.1186/1742-5573-1-3 |pages=3 |doi-access=free }}</ref> Hill himself said "None of my nine viewpoints can bring indisputable evidence for or against the cause-and-effect hypothesis and none can be required ''sine qua non''."<ref name="bh65" />

# '''Strength of Association''': A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal.<ref name="bh65" />
# '''Consistency of Data''': Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect.<ref name="bh65" />
# '''Specificity''': Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.<ref name="bh65" />
# '''Temporality''': The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay).<ref name="bh65" />
# '''Biological gradient''': Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.<ref name="bh65" />
# '''Plausibility''': A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge).<ref name="bh65" />
# '''Coherence''': Coherence between epidemiological and laboratory findings increases the likelihood of an effect. However, Hill noted that "... lack of such [laboratory] evidence cannot nullify the epidemiological effect on associations".<ref name="bh65" />
# '''Experiment''': "Occasionally it is possible to appeal to experimental evidence".<ref name="bh65" />
# '''Analogy''': The effect of similar factors may be considered.<ref name="bh65" />

=== Legal interpretation ===
[[Epidemiological study|Epidemiological studies]] can only go to prove that an agent could have caused, but not that it did cause, an effect in any particular case:

{{blockquote|Epidemiology is concerned with the [[Incidence (epidemiology)|incidence]] of disease in populations and does not address the question of the cause of an individual's disease. This question, sometimes referred to as specific causation, is beyond the domain of the science of epidemiology. Epidemiology has its limits at the point where an inference is made that the relationship between an agent and a disease is causal (general causation) and where the magnitude of excess risk attributed to the agent has been determined; that is, epidemiology addresses whether an agent can cause disease, not whether an agent did cause a specific plaintiff's disease.<ref name="green">{{cite book |last1= Green |first1= Michael D. |author2= D. Michal Freedman, and Leon Gordis |title= Reference Guide on Epidemiology |publisher= Federal Judicial Centre |url= http://www.fjc.gov/public/pdf.nsf/lookup/sciman06.pdf/$file/sciman06.pdf |access-date= 3 February 2008 |url-status= dead |archive-url= https://web.archive.org/web/20080227143925/http://www.fjc.gov/public/pdf.nsf/lookup/sciman06.pdf/$file/sciman06.pdf |archive-date= 27 February 2008 |df= dmy-all }}</ref>}}

In United States law, epidemiology alone cannot prove that a causal association does not exist in general. Conversely, it can be (and is in some circumstances) taken by US courts, in an individual case, to justify an inference that a causal association does exist, based upon a balance of [[probability]].

The subdiscipline of forensic epidemiology is directed at the investigation of specific causation of disease or injury in individuals or groups of individuals in instances in which causation is disputed or is unclear, for presentation in legal settings.