Fig. 2.

A is an example of a graphical causal model. The colored nodes are an example of a d-separation rule, where $I$ and $J$ are d-separated by $\{O_1\text{,}{O}_2\text{,}{O}_3\}$. B is the graphical causal model for our CSF data analysis example. Here, the population characteristics difference $E_P$ only has a direct causal effect on the age distribution. The sample selection bias $E_B$ is only directly related to diagnosis status $D$ for each specific study. Nodes denoting age and sex influence the CSF measurements denoted by $X$, which then influence the diagnosis status $D$. The CSF measurements $X$ and the nodes $E_P$ and $E_B$ are d-separated by diagnosis status $D$ and age.