gen_data_mapl

The DGP from section 6.4.1 in Zhou, Athey, and Wager (2023):
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:
\(Y \sim N(\mu_{a_i}(X_i), 4)\)

Learn optimal policies via doubly robust empirical
welfare maximization over trees. Given doubly robust reward estimates, this package
finds a rule-based treatment prescription policy, where the policy takes the form of
a shallow decision tree that is globally (or close to) optimal.

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

<dl><dt>n</dt>
<dd>Number of observations \(X\).</dd>
<dt>p</dt>
<dd>Number of features (minimum 7). Default is 10.</dd>
<dt>sigma2</dt>
<dd>Noise variance. Default is 4.</dd></dl>

Arguments

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

<dl>

<dt>n</dt>
<dd>Number of observations \(X\).</dd>


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


<dt>sigma2</dt>
<dd>Noise variance. Default is 4.</dd>

</dl>

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

Description

Usage

Value

Arguments

References