Yes, performing a predicted random sample over predicted future humans according to some model, or Bayesian distribution of models is fine — but in the case of the Bayesian model distribution case, if you have large uncertainty within your hypothesis distribution about how many there will be, that will dominate the results. What breaks causality is attempting to perform an actual random sample over the actual eventual number of future humans before that information is actually available, and then using frequentist typicality arguments based on that hypothetical invalid sampling process to try to smuggle information from the future into updating your hypothesis distribution.
Yes, performing a predicted random sample over predicted future humans according to some model, or Bayesian distribution of models is fine — but in the case of the Bayesian model distribution case, if you have large uncertainty within your hypothesis distribution about how many there will be, that will dominate the results. What breaks causality is attempting to perform an actual random sample over the actual eventual number of future humans before that information is actually available, and then using frequentist typicality arguments based on that hypothetical invalid sampling process to try to smuggle information from the future into updating your hypothesis distribution.