get_scores.multi_arm_causal_forest

Compute doubly robust (AIPW) scores for average treatment effect estimation
using a multi arm causal forest. Under regularity conditions, the average of the DR.scores
is an efficient estimate of the average treatment effect.

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

get_scores.multi_arm_causal_forest function

<dl><dt>forest</dt>
<dd>A trained multi arm causal forest.</dd>
<dt>subset</dt>
<dd>Specifies subset of the training examples over which we
estimate the ATE. WARNING: For valid statistical performance,
the subset should be defined only using features Xi, not using
the treatment Wi or the outcome Yi.</dd>
<dt>drop</dt>
<dd>If TRUE, coerce the result to the lowest possible dimension. Default is FALSE.</dd>
<dt>...</dt>
<dd>Additional arguments (currently ignored).</dd></dl>

Arguments

Compute doubly robust scores for a multi arm causal forest. — get_scores.multi_arm_causal_forest

<dl>

<dt>forest</dt>
<dd>A trained multi arm causal forest.</dd>


<dt>subset</dt>
<dd>Specifies subset of the training examples over which we
estimate the ATE. WARNING: For valid statistical performance,
the subset should be defined only using features Xi, not using
the treatment Wi or the outcome Yi.</dd>


<dt>drop</dt>
<dd>If TRUE, coerce the result to the lowest possible dimension. Default is FALSE.</dd>


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

</dl>

get_scores.multi_arm_causal_forest: Compute doubly robust scores for a multi arm causal forest.

Description

Usage

Value

Arguments