Formal Bayesian justification of conditional modelling

I'm having some trouble following the logic of this passage from Chapter 14 in Bayesian Data Analysis, A. Gelman:

The numerical 'data' in a regression problem includes both $$X$$ and $$y$$. Thus, a full Bayesian model includes a distribution for $$X$$, $$p(X|\psi$$), indexed by a parameter vector $$\psi$$, and thus involves a joint likelihood $$p(X,y|\psi,\theta)$$, along with a prior distribution, $$p(\psi,\theta)$$. In the standard regression context, the distribution of $$X$$ is assumed to provide no information about the conditional distribution of $$y$$ given $$X$$; that is, we assume prior independence of parameters $$\theta$$ determining $$p(y|X,\theta)$$ and the parameters $$\psi$$ determining $$p(X|\psi).$$

Thus, from a Bayesian perspective, the defining characteristic of a 'regression model' is that it ignores the information supplied by $$X$$ about ($$\psi$$, $$\theta$$). How can this be justified? Suppose $$\psi$$ and $$\theta$$ are independent in their prior distribution; that is $$p(\theta,\psi) = p(\theta)p(\psi)$$. Then the posterior distribution factors,

$$p(\psi,\theta|X,y) = p(\psi|X)p(\theta|X,y)$$, [...]

When I work this out I can't obtain the last line. I can get

$$p(\psi,\theta|X,y) = p(\psi|X,y,\theta)p(\theta|X,y)$$.

Intuitively the statement makes sense, but I can't prove to myself that it is true.

Once you've conditioned on $$X$$ there is no further information in $$Y$$ or $$\theta$$ concerning $$\psi$$, so $$p(\psi|X,\theta,y) = p(\psi|X)$$. This is a consequence of the independent priors on $$\theta$$ and $$\psi$$.
Concretely, in the full conditional distribution for $$\psi$$, which is proportional to the full joint distribution, you can factor out $$p(y|X,\theta)$$ and $$p(\theta)$$: $$p(\psi|X, \theta,y) \propto p(\psi)\;p(\theta)\;p(X|\psi)\;p(Y|X,\theta) \propto p(\psi) \;p(X|\psi)\propto p(\psi|X)$$
It would help here drawing a DAG (or causal diagram) representing the dependencies among the random variables $$\psi,\theta,X,y$$. It would be $$\psi \rightarrow X \rightarrow y \\ ~~~~~~~ \theta \nearrow$$Referring to this diagram will help with the calculations.
Then we can do the calculations \begin{align} p(\psi,\theta \mid X,y)&=&\frac{p(X,y\mid \psi,\theta) p(\psi)p(\theta)}{p(X,y)}\\ &=& \frac{p(y\mid X,\psi,\theta)\cdot p(X\mid\psi)p(\psi)p(\theta)}{p(y\mid X)p(X)} \\ &=& \frac{p(X\mid\psi)p(\psi)}{p(X)}\cdot \frac{p(y\mid X,\psi,\theta)p(\theta)}{p(y\mid X)} \\ &=& p(\psi\mid X)\cdot \frac{p(y,X,\psi,\theta)p(\theta)/ p(X,\psi,\theta)}{p(y\mid X)} \\ &=& p(\psi\mid X)\cdot\frac{p(\theta\mid y,X,\psi)p(y,X,\psi)p(\theta)}{p(y\mid X)p(X,\psi\mid \theta)p(\theta)}\\ &=& p(\psi\mid X)\cdot\frac{p(\theta\mid y,X,\psi)p(y\mid X,\psi)}{p(y\mid X)}\\ &=& p(\psi\mid X)\cdot p(\theta \mid X,y) \end{align} This could be compared with the frequentist argument in What is the difference between conditioning on regressors vs. treating them as fixed?.