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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 145.5 kB | win-64/r-policytree-1.1.1-r40ha856d6a_0.tar.bz2  4 years and 10 months ago 773 main
conda 136.4 kB | win-64/r-policytree-1.1.1-r41ha856d6a_0.tar.bz2  4 years and 10 months ago 780 main
conda 130.0 kB | osx-64/r-policytree-1.1.1-r40h9951f98_0.tar.bz2  4 years and 10 months ago 130 main
conda 129.9 kB | osx-64/r-policytree-1.1.1-r41h9951f98_0.tar.bz2  4 years and 10 months ago 115 main
conda 137.5 kB | linux-64/r-policytree-1.1.1-r40h03ef668_0.tar.bz2  4 years and 10 months ago 3308 main
conda 137.5 kB | linux-64/r-policytree-1.1.1-r41h03ef668_0.tar.bz2  4 years and 10 months ago 3204 main

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