Would you mind sending along your pdf?

I'll find somewhere to stick it and link it to the post.

The somewhat harder bullet to swallow is the assumption that the random variables are Markovian; that is, they are "memoryless" in the sense that only the present state determines the future.

This is not quite correct as a description of the SGS-Pearl system.

Yeah, sloppy writing on my part; the "time" that appears here is only an observer's sequence of observations of the system's state. I agree with what you say about discrete-time models.

On its face, stating the usual global causal Markov axiom -- but assuming densely-ordered times, rather than non-densely-ordered times, is non-obvious.

What was assumed there is not that, because in this development I have not gotten to the point of finding a directed graph of variables anywhere. Presumably I'll need a local corollary of Pearl's theorem 1.4.1, i.e., every causal flow model (probably subject to some technical restrictions) locally induces a graph model with a compatible joint probability distribution. This has some hope of succeeding; if a distribution is consistent with a graph model, then small perturbations of it are also consistent with it.

This is what I meant to assume: that X is a continuous-time markov process.