Correct estimators in DMC without forward walking?

Computing properties other than the energy using Diffusion Monte Carlo requires forward walking to get correct answers. That may change, if this paper - Hellman-Feynman operator sampling in Diffusion Monte Carlo calculations - is correct.

Although as I look at the paper more, the technique doesn't look any easier than the forward walking implementation by Casulleras and Boronat (the paper does mention this similarity). It appears to use similar data in a different combination. I would be interested to see how the noise, bias, and stability characteristics compare.

The Hellman-Feynman theorem seems to be getting lots of attention in the QMC world recently. It's also been used to construct Zero-Variance Zero-Bias estimators (most recently example here).