pop_data_full

Extended World Value Survey (WVS) Data for India in 1995, 2001, 2006, 2012,
and 2022.

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

pop_data_full function

<div class="section" id="pop-data-full">
<h3><code>pop_data_full</code></h3>
A data frame with 11,813 rows and 13 columns:<dl>
<dt>female</dt>
<dd>Respondent’s Sex.</dd><dt>age</dt>
<dd>Age of respondent.</dd><dt>income</dt>
<dd>income group of Household.</dd><dt>religion</dt>
<dd>Religious denomination</dd><dt>practicing_religion</dt>
<dd>Importance of religion in respondent’s life.</dd><dt>education</dt>
<dd>Educational level of respondent.</dd><dt>political_ideology</dt>
<dd>Political ideology of respondent.</dd><dt>employment</dt>
<dd>Employment status and full-time employee.</dd><dt>marital_status</dt>
<dd>Marital status of respondent.</dd><dt>job_loss</dt>
<dd>Concern about job loss.</dd><dt>support_war</dt>
<dd>Binary (Yes/No) outcome measure for willingness to fight war.</dd><dt>strong_leader</dt>
<dd>Binary measure of preference for strong leader.</dd>...</dl></div>

Format

World Value Survey India Sample — pop_data_full

<div class='section' id='pop-data-full'>
<h3><code>pop_data_full</code></h3>


A data frame with 11,813 rows and 13 columns:<dl>
<dt>female</dt>
<dd>Respondent’s Sex.</dd>

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

<dt>income</dt>
<dd>income group of Household.</dd>

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

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

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

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

<dt>employment</dt>
<dd>Employment status and full-time employee.</dd>

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

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

<dt>support_war</dt>
<dd>Binary (Yes/No) outcome measure for willingness to fight war.</dd>

<dt>strong_leader</dt>
<dd>Binary measure of preference for strong leader.</dd>

...

</dl>

</div>

pop_data_full: World Value Survey India Sample

Description

Usage

Arguments

Format

`pop_data_full`