Description Usage Arguments Examples
U-learner as proposed by Kunzel, Sekhon, Bickel, and Yu (2017), implemented via xgboost (boosting)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
x |
the input features |
w |
the treatment variable (0 or 1) |
y |
the observed response (real valued) |
k_folds |
number of folds used for cross fitting and cross validation |
p_hat |
pre-computed estimates on E[W|X] corresponding to the input x. uboost will compute it internally if not provided. |
m_hat |
pre-computed estimates on E[Y|X] corresponding to the input x. uboost will compute it internally if not provided. |
cutoff |
the threshold to cutoff propensity estimate |
ntrees_max |
the maximum number of trees to grow for xgboost |
num_search_rounds |
the number of random sampling of hyperparameter combinations for cross validating on xgboost trees |
print_every_n |
the number of iterations (in each iteration, a tree is grown) by which the code prints out information |
early_stopping_rounds |
the number of rounds the test error stops decreasing by which the cross validation in finding the optimal number of trees stops |
nthread |
the number of threads to use. The default is NULL, which uses all available threads |
verbose |
boolean; whether to print statistic |
1 2 3 4 5 6 7 8 9 10 11 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.