The Janus face of statistical adjustment: confounders versus colliders

It has long been established that controlling for confounders is essential to delineate the causal relationship between exposure and disease. For this purpose, statistical adjustment is widely used in observational studies. However, many researchers don't acknowledge the potential pitfalls of statistical adjustment. The aim of the present paper was to demonstrate that statistical adjustment is a double edged sword. By using numerically identical examples, we show that adjustment for a common consequence of the exposure and the outcome can lead to as much bias as absence of necessary adjustment for a confounder.