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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-staging / r-policytree
Type Size Name Uploaded Downloads Labels
conda 149.6 kB | win-64/r-policytree-1.2.1-r41ha856d6a_1.conda  1 year and 7 months ago 356 main
conda 144.8 kB | osx-64/r-policytree-1.2.1-r42hac7d2d5_1.conda  1 year and 7 months ago 150 main
conda 144.1 kB | osx-64/r-policytree-1.2.1-r43hac7d2d5_1.conda  1 year and 7 months ago 143 main
conda 151.9 kB | linux-64/r-policytree-1.2.1-r42ha503ecb_1.conda  1 year and 7 months ago 1300 main
conda 151.4 kB | linux-64/r-policytree-1.2.1-r43ha503ecb_1.conda  1 year and 7 months ago 1244 main
conda 149.3 kB | win-64/r-policytree-1.2.1-r41ha856d6a_0.conda  2 years and 2 months ago 643 main
conda 143.7 kB | osx-64/r-policytree-1.2.1-r41h49197e3_0.conda  2 years and 2 months ago 233 main
conda 143.7 kB | osx-64/r-policytree-1.2.1-r42h49197e3_0.conda  2 years and 2 months ago 241 main
conda 152.3 kB | linux-64/r-policytree-1.2.1-r41h7525677_0.conda  2 years and 2 months ago 1648 main
conda 152.7 kB | linux-64/r-policytree-1.2.1-r42h7525677_0.conda  2 years and 2 months ago 1660 main

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