Erik Sverdrup

The logic above is regarding R to C++, if Rcpp supported move semantics into their containers, the pass trough from C++ to R could possibly be quicker? (instead of copying...

A sketch together with @davidahirshberg on one workaround for R/C++ copy of all the Tree vector members: From the C++ side: create vectors with a new subclass of std::vector which...

I think we can scrap this memory plug-and-play attempt: Dirk Eddelbuettel says it is probably not possible. Even if it was possible, it would mess with R internals that could...

Hi @carlyls, if you are predicting on a new test-sample, GRF needs to re-serialize the entire forest to pass it back to C++, thus it'll require twice the memory (one...

Yes, you can use any good prediction method to predict Y.hat and W.hat. If you know the treatment propensities you can just supply that instead.

Hi @LuqianSun, yes setting clusters for W.hat and Y.hat makes sense if you are setting them for causal forest. The online reference has a section on clustering here: https://grf-labs.github.io/grf/REFERENCE.html#cluster-robust-estimation

Hi @fangjiany, could you give a simulation/data example for this?

Hi @panqinglzmc, the tool you'd use for this depends on the question you are asking. If you want a linear association summary of the CATE that has good semi-parametric inferential...

Hi @ukwarwicker, a straight-forward way to apply GRF in the standard DiD setting is to use causal forest on the differenced outcomes: #1064. (If the setting is you don't observe...

Hi @zmarkovich, could you please elaborate what you intend to do?