control_drql

<code>control_drql</code> sets the default control arguments
for doubly robust Q-learning, <code>type = "drql"</code>.

Package for learning and evaluating (subgroup) policies via doubly robust loss functions. Policy learning methods include doubly robust blip/conditional average treatment effect learning and sequential policy tree learning. Methods for (subgroup) policy evaluation include doubly robust cross-fitting and online estimation/sequential validation. See Nordland and Holst (2022) <doi:10.48550/arXiv.2212.02335> for documentation and references.

Andreas Nordland

polle

Policy Learning

Klaus Holst

control_drql function

<dl><dt>qv_models</dt>
<dd>Single element or list of V-restricted Q-models created
by <code>q_glm()</code>, <code>q_rf()</code>, <code>q_sl()</code> or similar functions.</dd></dl>

Arguments

Control arguments for doubly robust Q-learning — control_drql

<dl>

<dt>qv_models</dt>
<dd>Single element or list of V-restricted Q-models created
by <code>q_glm()</code>, <code>q_rf()</code>, <code>q_sl()</code> or similar functions.</dd>

</dl>

control_drql: Control arguments for doubly robust Q-learning

Description

Usage

Value

Arguments