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Learn optimal policies via doubly robust empirical welfare maximization over trees. This package implements the multi-action doubly robust approach of Zhou, Athey and Wager (2018) <arXiv:1810.04778> in the case where we want to learn policies that belong to the class of depth k decision trees.

copied from cf-post-staging / r-policytree
Type Size Name Uploaded Downloads Labels
conda 149.4 kB | win-64/r-policytree-1.2.2-r41ha856d6a_0.conda  2 years and 3 months ago 476 main
conda 144.6 kB | osx-64/r-policytree-1.2.2-r42hac7d2d5_0.conda  2 years and 3 months ago 256 main
conda 144.3 kB | osx-64/r-policytree-1.2.2-r43hac7d2d5_0.conda  2 years and 3 months ago 250 main
conda 150.8 kB | linux-64/r-policytree-1.2.2-r43ha503ecb_0.conda  2 years and 3 months ago 1904 main
conda 151.4 kB | linux-64/r-policytree-1.2.2-r42ha503ecb_0.conda  2 years and 3 months ago 1901 main

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