If you're seeing this message, it means we're having trouble loading external resources on our website. Show If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked. We are currently in the process of updating this chapter and we appreciate your patience whilst this is being completed.
A principal aim of epidemiology is to assess the causes of disease. However, since most epidemiological studies are by nature observational rather than experimental, a number of possible explanations for an observed association need to be considered before we can infer that a cause-effect relationship exists. Specifically, causation needs to be distinguished from mere association – the link between two variables (often an exposure and an outcome). An observed association may in fact be due to the effects of one or more of the following:
A discussion of chance, bias and confounding can be found in the subsequent chapters and in the chapter “Sources of variation”. Reverse causality describes the event where an association between an exposure and an outcome is not due to direct causality from exposure to outcome, but rather because the defined “outcome” actually results in a change in the defined “exposure”. For example, a study may find an association between using recreational drugs (exposure) and poor mental wellbeing (outcome) and thus conclude that using drugs is likely to impair wellbeing. A reverse causation explanation could be that people with poor mental wellbeing are more likely to use recreational drugs as, say, a means of escapism.
Judging Causality An observed statistical association between a risk factor and a disease does not necessarily lead us to infer a causal relationship; conversely, the absence of an association does not necessarily imply the absence of a causal relationship. A judgment about whether an observed statistical association represents a cause-effect relationship between exposure and disease requires inferences far beyond the data from a single study. The Bradford Hill criteria, listed below, are widely used in epidemiology as a framework with which to assess whether an observed association is likely to be causal.1
Although widely used, the criteria are not without criticism. Rothman argues that Hill did not propose these criteria as a checklist for evaluating whether a reported association might be interpreted as causal, but they have been widely applied in this way. He contends that the Bradford Hill criteria fail to deliver on the hope of clearly distinguishing causal from non-causal relations.2 For example, the first criterion 'strength of association' does not take into account the fact that not every component cause will have a strong association with the disease it produces, or that strength of association also depends on the prevalence of other factors.2 In terms of the third criterion, 'specificity', which suggests that a relationship is more likely to be causal if the exposure is related to a single outcome, Rothman argues that this criterion is misleading as a cause may have many effects, for example smoking.2 The fifth criterion, ‘biological gradient’ suggests that the plausibility of a causal association is increased if a dose-response curve can be demonstated.3 However, such relationships may also result from confounding or other biases.2,3 According to Rothman, the only criterion that can be considered as a true causal criterion is 'temporality', that is that the cause precedes the effect. It may be difficult, however, to ascertain the time sequence for cause and effect.2 The process of causal inference is complex and arriving at a tentative inference of a causal or non-causal nature of an association is a subjective process. For a comprehensive discussion on causality, refer to Rothman.2 What are the requirements for determining if a relationship is causal?In sum, the following criteria must be met for a correlation to be considered causal:. The two variables must vary together.. The relationship must be plausible.. The cause must precede the effect in time.. The relationship must be nonspurious (not due to a third variable).. What are the 3 criteria for causality?There are three conditions for causality: covariation, temporal precedence, and control for “third variables.” The latter comprise alternative explanations for the observed causal relationship.
What are the 3 criteria for establishing a causal relationship quizlet?The three criteria for establishing causation are covariance, temporal precedence, and internal validity. Bivariate correlations will always fall short in at least one of these categories because correlation does not = causation. There can be a third variable as well.
Which of the following are requirements for identifying a causal effect quizlet?Association, time order, and nonspuriousness are required to establish a causal effect. In experiments, subjects are ideally assigned to control and experimental groups using simple random sampling. The ex post facto control group design is a quasi-experimental design.
|