plot.rank_average_treatment_effect

Plot the Targeting Operator Characteristic curve.

Forest-based statistical estimation and inference.
GRF provides non-parametric methods for heterogeneous treatment effects estimation
(optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables),
as well as least-squares regression, quantile regression, and survival regression,
all with support for missing covariates.

Erik Sverdrup

Generalized Random Forests

Julie Tibshirani

Susan Athey

Rina Friedberg

Vitor Hadad

David Hirshberg

Luke Miner

Stefan Wager

Marvin Wright

plot.rank_average_treatment_effect function

<dl><dt>x</dt>
<dd>The output of rank_average_treatment_effect.</dd>
<dt>...</dt>
<dd>Additional arguments passed to plot.</dd>
<dt>ci.args</dt>
<dd>Additional arguments passed to points.</dd>
<dt>abline.args</dt>
<dd>Additional arguments passed to abline.</dd>
<dt>legend.args</dt>
<dd>Additional arguments passed to legend.</dd></dl>

Arguments

Plot the Targeting Operator Characteristic curve. — plot.rank_average_treatment_effect

<dl>

<dt>x</dt>
<dd>The output of rank_average_treatment_effect.</dd>


<dt>...</dt>
<dd>Additional arguments passed to plot.</dd>


<dt>ci.args</dt>
<dd>Additional arguments passed to points.</dd>


<dt>abline.args</dt>
<dd>Additional arguments passed to abline.</dd>


<dt>legend.args</dt>
<dd>Additional arguments passed to legend.</dd>

</dl>

plot.rank_average_treatment_effect: Plot the Targeting Operator Characteristic curve.

Description

Usage

Arguments