gen_data_mapl

<p>Number of features (minimum 7). Default is 10.</p>

sigma2

<p>The DGP from section 6.4.1 in Zhou, Athey, and Wager (2018):
There are \(d=3\) actions \((a_0,a_1,a_2)\) which depend
on 3 regions the covariates \(X \sim U[0,1]^p\) reside in. Observed outcomes:
\(Y \sim N(\mu_{a_i}(X_i), 4)\)</p>

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.

Erik Sverdrup

policytree

Policy Learning via Doubly Robust Empirical Welfare Maximization
over Trees

Ayush Kanodia

Zhengyuan Zhou

Susan Athey

Stefan Wager

gen_data_mapl function

Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization — gen_data_mapl

Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization

gen_data_mapl: Example data generating process from Offline Multi-Action Policy Learning: Generalization and Optimization

Description

Usage

Arguments

Value

References