I am trying to understand the practicalities of Pearl's front-door criteria to estimate causal effects under an unobserved unconfounder. First, to give context to the question we have a graph that looks like this:
Where $U$ is unobserved and, hence, no back-door adjustment formula can be used.
We can infer the causal effects on $Y$ of an intervention $do(X=x)$ by using the front-door adjustment formula Pearl (2009 p.81-83):
$$P(y | \textit{do}(X = x)) = \sum_z P(z | x) \sum_{x'} P(y|x', z)P(x').$$
My question is: What is the intuition of conditioning $Y$ on $x'$ ($P(y|x', z)$) if $Y$ is conditionally independent of $X$ given $Z$? Does that mean, in practical terms, that the way to estimate the front-door adjustment is by using the following:
$$P(y | \textit{do}(X = x)) = \sum_z P(z | x) \sum_{x'} P(y|z)P(x').$$