neuralnet_complier_mod

Train model using group exposed to treatment with compliance as binary
outcome variable and covariates.

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

neuralnet_complier_mod function

<dl><dt>complier.formula</dt>
<dd>formula for complier variable as outcome and
covariates (c ~ x)</dd>
<dt>exp.data</dt>
<dd><code>data.frame</code> for experimental data.</dd>
<dt>treat.var</dt>
<dd>string for treatment variable.</dd>
<dt>algorithm</dt>
<dd>string for algorithm for training neural networks.
Default set to the Resilient back propagation with weight backtracking
(rprop+). Other algorithms include backprop', rprop-', 'sag', or 'slr'
(see <code>neuralnet</code> package).</dd>
<dt>hidden.layer</dt>
<dd>vector for specifying hidden layers and number of neurons.</dd>
<dt>ID</dt>
<dd>string for identifier variable</dd>
<dt>stepmax</dt>
<dd>maximum number of steps.</dd></dl>

Arguments

Train compliance model using neural networks — neuralnet_complier_mod

<dl>

<dt>complier.formula</dt>
<dd>formula for complier variable as outcome and
covariates (c ~ x)</dd>


<dt>exp.data</dt>
<dd><code>data.frame</code> for experimental data.</dd>


<dt>treat.var</dt>
<dd>string for treatment variable.</dd>


<dt>algorithm</dt>
<dd>string for algorithm for training neural networks.
Default set to the Resilient back propagation with weight backtracking
(rprop+). Other algorithms include backprop', rprop-', 'sag', or 'slr'
(see <code>neuralnet</code> package).</dd>


<dt>hidden.layer</dt>
<dd>vector for specifying hidden layers and number of neurons.</dd>


<dt>ID</dt>
<dd>string for identifier variable</dd>


<dt>stepmax</dt>
<dd>maximum number of steps.</dd>

</dl>

neuralnet_complier_mod: Train compliance model using neural networks

Description

Usage

Value

Arguments