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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 148.9 kB | osx-64/r-policytree-1.2.4-r44h384437d_0.conda  3 months and 16 days ago 32 main
conda 148.7 kB | osx-64/r-policytree-1.2.4-r45h384437d_0.conda  3 months and 16 days ago 28 main
conda 150.5 kB | win-64/r-policytree-1.2.4-r45hd8a2815_0.conda  3 months and 16 days ago 44 main
conda 150.8 kB | win-64/r-policytree-1.2.4-r44hd8a2815_0.conda  3 months and 16 days ago 45 main
conda 156.4 kB | linux-64/r-policytree-1.2.4-r44h3697838_0.conda  3 months and 16 days ago 317 main
conda 156.3 kB | linux-64/r-policytree-1.2.4-r45h3697838_0.conda  3 months and 16 days ago 284 main

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