fit_c_functions

Fits right-censoring models for each stage or a single model across all stages.

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

fit_c_functions function

<dl><dt>policy_data</dt>
<dd>Policy data object created by <code>policy_data()</code></dd>
<dt>c_models</dt>
<dd>Single c_model or list of K+1 c_models</dd>
<dt>full_history</dt>
<dd>Logical; use full history (TRUE) or Markov-type history (FALSE)</dd></dl>

Arguments

Fit Censoring Functions — fit_c_functions

<dl>

<dt>policy_data</dt>
<dd>Policy data object created by <code>policy_data()</code></dd>


<dt>c_models</dt>
<dd>Single c_model or list of K+1 c_models</dd>


<dt>full_history</dt>
<dd>Logical; use full history (TRUE) or Markov-type history (FALSE)</dd>

</dl>

fit_c_functions: Fit Censoring Functions

Description

Usage

Value

Arguments

Details