Bootstrap estimator for ORF

Hi! I'm using bootstrap to derive confidence interval for ORF. I notice that the bootstrap estimator relies on random sampling with replacement. This raises a concern with respect to ORF (forest-based) estimation. Because of sampling with replacement, it is likely that one single observation is sampled more than once for an ORF estimation process. This means that identical observations may be used for training the tree/forest/weights. Specifically, the identical observations will definitely go into the same leaf. This looks somehow wired, though I don't have a clear prediction on how this will influence estimating results.

I checked other forest-based methods, such as random forest, generalized random forest proposed by Athey et al. It seems that when growing trees repeatedly, random forest allows sampling either with or without replacement, while generalized random forest (causal forest) only allows sampling without replacement. As for ORF, I guess sampling without replacement is more suitable. Any suggestions on this? Thanks!