Erik Sverdrup

Hi @PhilipSpechler, n=10 000 is probably too much to visualize unless each tree is very shallow. If a simple tree-based treatment assignment policy is what you're after, you can have...

You can gauge a regression_forest forest's fit with its MSE. You can tune them if you want, `num.trees` are 500 by default for W.hat and Y.hat.

Btw, it's great you have questions on GRF, but maybe if you have many you could collect them all in one issue? (That makes it easier to remember to respond),...

Hi @hhsievertsen, I'll instead suggest an answer to a slightly modified question that you could ideally use instead: "given CATEs estimated on a training set X.train, do they do a...

Just adding these code references for future reference, the estimator looks like an AIPW-style thing we could add to GRF https://github.com/serobertson/ExtendingInferences [Extending inferences from a randomized trial to a new...

Hi @hhsievertsen, we've added a simple vignette describing a function you could copy/paste to estimate ATEs + SEs on a new test set here: https://grf-labs.github.io/grf/articles/ate_transport.html

Hi @hhsievertsen, fitting the p-model on the full target sample first sounds like the way to go the get best p-hat estimates, then you can subset afterwards for the subgroup...

Sorry, I think I misread your first post, if by subsample you mean you want the ATE for `X.test.subset = X.test[subset, ]`, then you can just use the vignette example...

For reference ranger does this here https://github.com/imbs-hl/ranger/blob/master/src/Forest.cpp#L1072

Hi @lucatrapin, section 4 in https://arxiv.org/pdf/1610.01271.pdf describes the approach to CIs (bootstrap of little bags). A short description is in the online reference: https://grf-labs.github.io/grf/REFERENCE.html#variance-estimates