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Graphical model: Whereas a mediator is a factor in the causal chain (top), a confounder is a spurious factor incorrectly implying causation (bottom). In statistics, a spurious relationship or spurious correlation [1] [2] is a mathematical relationship in which two or more events or variables are associated but not causally related, due to either coincidence or the presence of a certain third ...
Correlation is a valuable type of scientific evidence in fields such as medicine, psychology, and sociology. Correlations must first be confirmed as real, and every possible causative relationship must then be systematically explored.
Typical criteria for inferring a causal relationship includes: i) a statistical association between the two variables ii) the direction of influence (that changes in the causal factor induce change in the dependent variable) and; iii) a requirement that the relationship between variables is non-spurious. [3]
Simple mediation model. The independent variable causes the mediator variable; the mediator variable causes the dependent variable. In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator ...
However, the presence of these covariates can induce spurious effects when either (1) the covariate (C) is correlated with one of the primary variables of interest (e.g. variable A or B), or (2) when the covariate itself is a moderator of the correlation between either A or B with Y.
Causality is an influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. [1]
When enough hypotheses are tested, it is virtually certain that some will be reported to be statistically significant (even though this is misleading), since almost every data set with any degree of randomness is likely to contain (for example) some spurious correlations. If they are not cautious, researchers using data mining techniques can be ...
Judea Pearl defines a causal model as an ordered triple ,, , where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U ...