Collider
A collider is a variable that is caused by multiple variables.
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Description
Colliders are closely related to confounders, but in the 'opposite' way. Generically, consider a variable Z that is associated with two independent variables, X and Y. If Z is controlled for, X and Y become associated.
The presence of a collider causes collider bias or Berkson's paradox.
Alex Dimakis provides a more concrete example: "Assume that to be a successful actor you have to be either extremely good looking or extremely talented. Assume also that talent and looks are independent in the population. However, among sucessful [sic] actors you will observe a negative correlation between looks and talent."
See in the following demo that controlling for Z 'creates' a correlation. (Note that in R, the Bernoulli distribution is handled as a special case of the Binomial distribution. Comparing the sum of X and Y to 0 is effectively checking if either is 1.)
> X <- rbinom(1000, 1, 0.5) > Y <- rbinom(1000, 1, 0.5) > Z <- rbinom(1000, 1, ifelse(X+Y>0, 0.9, 0.2)) > cor(X,Y) [1] -0.02387166 > cor(X[Z==1], Y[Z==1]) [1] -0.3387377 > cor(X[Z==0], Y[Z==0]) [1] 0.2764379