I feel a bit uneasy with how I've mentally dealt with Borel's paradox and other associated "paradoxes" dealing with conditional probability. For those who are reading this that aren't familiar with it, see this link. My mental response up to this point has been mostly to ignore it because no one seems to talk about it, but I feel I should rectify this.
We know that this paradox exists, and yet it seems like in practice (as an extreme example, Bayesian analysis) we are perfectly fine with conditioning on events of measure $0$; if $X$ is my data, we condition on $X = x$ all the time, even though this is an event of measure $0$ when $X$ is continuous. And we certainly make no effort to construct a sequence of events converging to the event we observed to resolve the paradox, at least not explicitly.
I think this is okay because we have essentially fixed the random variable $X$ (in principle) before the experiment, and so we are conditioning on $\sigma(X)$. That is, $\sigma(X)$ is the natural $\sigma$-algebra to condition on because the information $X = x$ is coming to use through $X$ - if it had come to us in some other fashion, we would condition on a different $\sigma$-algebra. Borel's paradox arises because (I guess) it isn't obvious what the appropriate $\sigma$-algebra to condition on, but the Bayesian has specified $\sigma(X)$. Because we are specifying a priori that the information $X = x$ came to us by means of measuring $X$ we are in the clear. Once we have specified the $\sigma$-algebra, everything is fine; we construct our conditional expectation using Radon-Nikodym and everything is unique up-to null sets.
Is this essentially right, or am I way off? If I'm way off, what is the justification for behaving as we do? [Given the Q&A nature of this site, regard this as my question.] When I took my measure-theoretic probability we, for some reason I don't understand, never even touched conditional expectation. As a result, I'm worried that my ideas are very confused.