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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 137.0 kB | win-64/r-policytree-1.1.0-r41ha856d6a_0.tar.bz2  4 years and 11 months ago 782 main
conda 146.0 kB | win-64/r-policytree-1.1.0-r40ha856d6a_0.tar.bz2  4 years and 11 months ago 788 main
conda 130.0 kB | osx-64/r-policytree-1.1.0-r40h9951f98_0.tar.bz2  4 years and 11 months ago 134 main
conda 130.1 kB | osx-64/r-policytree-1.1.0-r41h9951f98_0.tar.bz2  4 years and 11 months ago 135 main
conda 137.8 kB | linux-64/r-policytree-1.1.0-r41h03ef668_0.tar.bz2  4 years and 11 months ago 3252 main
conda 137.7 kB | linux-64/r-policytree-1.1.0-r40h03ef668_0.tar.bz2  4 years and 11 months ago 3330 main

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