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.

A pluggable package for forest-based statistical estimation and inference.
GRF currently provides methods for non-parametric least-squares regression,
quantile regression, survival regression and treatment effect estimation (optionally using instrumental
variables), with support for missing values.

Julie Tibshirani

Generalized Random Forests

Susan Athey

Rina Friedberg

Vitor Hadad

David Hirshberg

Luke Miner

Erik Sverdrup

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>...</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>...</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