Causal Inference In Statistics by Pearl, section 3.5, page 70 clearly mentions that -
This effect, written $P(Y=y|do(X=x),Z=z)$, measures the distribution of $Y$ in a subset of the population for which $Z$ achieves the value $z$ after the intervention.
In that context Rule 2 says -
$P(Y=y|do(X=x),Z=z)$
$= \sum_s {P(Y=y|X=x,S=s,Z=z)P(S=s|Z=z)}$
where $S \cup Z$ satisfies the backdoor criterion.
Query: Is $Z=z$ in the second expression pre- or post-intervention measure?
Note: In subsequent section where the effect of conditional intervention is evaluated, it says -
$P(Y=y|do(X=g(Z)))$
$=\sum_z{P(Y=y|do(X=g(Z)),Z=z)P(Z=z|do(X=g(Z)))}$
$=\sum_z{P(Y=y|do(X=g(z)),Z=z)P(Z=z)}$
The equality $P(Z=z|do(X=g(Z)))=P(Z=z)$ stems, of course, from the fact that $Z$ occurs before $X$; hence any control exerted on $X$ can have no effect on the distribution of $Z$.
Here it seems that $Z=z$ is a pre-intervention measure. Hence, in the above sections, when is $Z-z$ a pre-intervention measure and when is it a post-intervention measure?