create_ml_args_causalforest

Create arguments for causal forest

Provides various statistical methods for evaluating
Individualized Treatment Rules under randomized data. The provided
metrics include Population Average Value (PAV), Population Average
Prescription Effect (PAPE), Area Under Prescription Effect Curve
(AUPEC). It also provides the tools to analyze Individualized
Treatment Rules under budget constraints. Detailed reference in Imai
and Li (2019) <arXiv:1905.05389>.

Michael Lingzhi Li

evalITR

Evaluating Individualized Treatment Rules

Kosuke Imai

Jialu Li

Xiaolong Yang

create_ml_args_causalforest function

<dl><dt>data</dt>
<dd>A dataset</dd></dl>

Arguments

Create arguments for causal forest — create_ml_args_causalforest

<dl>

<dt>data</dt>
<dd>A dataset</dd>

</dl>

create_ml_args_causalforest: Create arguments for causal forest

Description

Usage

Arguments