Collider bias

Adjust for a variable caused by both treatment and outcome, then compare the estimate with the known true effect.

Why can adjusting for more variables create bias?

A collider is a common effect of two variables. Conditioning on it can make its causes associated within the adjusted comparison, opening a noncausal path that was previously closed.

Try it in the experiment

  1. Start with adjustment for the risk score only.
  2. Also account for the follow-up score.
  3. Watch the estimate move even though the data-generating world stays fixed.

In this example, treatment and outcome both cause the later follow-up score. The score does not cause the outcome. Its availability in the data does not make it a valid adjustment variable.