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

In grf: Generalized Random Forests

Compute doubly robust scores for a multi arm causal forest.

Description

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.

Usage

## S3 method for class 'multi_arm_causal_forest'
get_scores(forest, subset = NULL, drop = FALSE, ...)

Arguments

forest	A trained multi arm causal forest.
subset	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.
drop	If TRUE, coerce the result to the lowest possible dimension. Default is FALSE.
...	Additional arguments (currently ignored).

Value

An array of scores for each contrast and outcome.

grf documentation built on Oct. 1, 2023, 1:07 a.m.