exp_data_full

Extended experiment data with 514 observations

dataset

Functions to estimate Conditional Average Treatment Effects (CATE)
and Population Average Treatment Effects on the Treated (PATT) from
experimental or observational data using the Super Learner (SL) ensemble
method and Deep neural networks. The package first provides functions to
implement meta-learners such as the Single-learner (S-learner) and
Two-learner (T-learner) described in KC<nzel et al. (2019)
<doi:10.1073/pnas.1804597116> for estimating the CATE. The S- and T-learner
are each estimated using the SL ensemble method and deep neural networks. It
then provides functions to implement the Ottoboni and Poulos (2020)
<doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from
experimental data with noncompliance by using the SL ensemble method and
deep neural networks.

Nguyen K. Huynh

DeepLearningCausal

Causal Inference with Super Learner and Deep Neural Networks

Bumba Mukherjee

Yang Yang

exp_data_full function

<div class="section" id="exp-data-full">
<h3><code>exp_data_full</code></h3>
A data frame with 514 rows and 12 columns:<dl>
<dt>female</dt>
<dd>Gender.</dd><dt>age</dt>
<dd>Age of participant.</dd><dt>income</dt>
<dd>Monthly household income.</dd><dt>religion</dt>
<dd>Religious denomination</dd><dt>practicing_religion</dt>
<dd>Importance of religion in life.</dd><dt>education</dt>
<dd>Educational level of participant.</dd><dt>political_ideology</dt>
<dd>Political ideology of participant.</dd><dt>employment</dt>
<dd>Employment status of participant.</dd><dt>marital_status</dt>
<dd>Marital status of participant.</dd><dt>job_loss</dt>
<dd>Concern about job loss.</dd><dt>strong_leader</dt>
<dd>Binary treatment measure of leader type.</dd><dt>support_war</dt>
<dd>Binary outcome measure for willingness to fight war.</dd>#' ...</dl></div>

Format

Survey Experiment of Support for Populist Policy — exp_data_full

<div class='section' id='exp-data-full'>
<h3><code>exp_data_full</code></h3>


A data frame with 514 rows and 12 columns:<dl>
<dt>female</dt>
<dd>Gender.</dd>

<dt>age</dt>
<dd>Age of participant.</dd>

<dt>income</dt>
<dd>Monthly household income.</dd>

<dt>religion</dt>
<dd>Religious denomination</dd>

<dt>practicing_religion</dt>
<dd>Importance of religion in life.</dd>

<dt>education</dt>
<dd>Educational level of participant.</dd>

<dt>political_ideology</dt>
<dd>Political ideology of participant.</dd>

<dt>employment</dt>
<dd>Employment status of participant.</dd>

<dt>marital_status</dt>
<dd>Marital status of participant.</dd>

<dt>job_loss</dt>
<dd>Concern about job loss.</dd>

<dt>strong_leader</dt>
<dd>Binary treatment measure of leader type.</dd>

<dt>support_war</dt>
<dd>Binary outcome measure for willingness to fight war.</dd>

#' ...

</dl>

</div>

exp_data_full: Survey Experiment of Support for Populist Policy

Description

Usage

Arguments

Format

`exp_data_full`