Collider (statistics)

Conditioning on the collider via regression analysis, stratification, experimental design, or sample selection based on values of the collider creates a non-causal association between X and Y (Berkson's paradox).

In the terminology of causal graphs, conditioning on the collider opens the path between X and Y.

Unlike colliders, confounder variables should be controlled for when estimating causal associations.

[citation needed] To detect and manage collider bias, scholars have made use of directed acyclic graphs.

[7] Randomization and quasi-experimental research designs are not useful in overcoming collider bias.