print.rank_average_treatment_effect

Print the Rank-Weighted Average Treatment Effect (RATE).

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

print.rank_average_treatment_effect function

<dl><dt>x</dt>
<dd>The output of rank_average_treatment_effect.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Print the Rank-Weighted Average Treatment Effect (RATE). — print.rank_average_treatment_effect

<dl>

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


<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd>

</dl>

print.rank_average_treatment_effect: Print the Rank-Weighted Average Treatment Effect (RATE).

Description

Usage

Arguments