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

complier_mod function

<dl><dt>exp.data</dt>
<dd>list object of experimental data.</dd>
<dt>complier.formula</dt>
<dd>formula to fit compliance model (c ~ x) using
complier variable and covariates</dd>
<dt>treat.var</dt>
<dd>string specifying the binary treatment variable</dd>
<dt>ID</dt>
<dd>string for name of indentifier variable.</dd>
<dt>SL.learners</dt>
<dd>vector of strings for ML classifier algorithms. If left
<code>NULL</code> employs extreme gradient boosting, elastic net regression, random
forest, and neural nets.</dd></dl>

Arguments

Train complier model using ensemble methods — complier_mod

<dl>

<dt>exp.data</dt>
<dd>list object of experimental data.</dd>


<dt>complier.formula</dt>
<dd>formula to fit compliance model (c ~ x) using
complier variable and covariates</dd>


<dt>treat.var</dt>
<dd>string specifying the binary treatment variable</dd>


<dt>ID</dt>
<dd>string for name of indentifier variable.</dd>


<dt>SL.learners</dt>
<dd>vector of strings for ML classifier algorithms. If left
<code>NULL</code> employs extreme gradient boosting, elastic net regression, random
forest, and neural nets.</dd>

</dl>

complier_mod: Train complier model using ensemble methods

Description

Usage

Value

Arguments