pattc_counterfactuals

Create counterfactual datasets in the population for compliers and
noncompliers. Then predict potential outcomes from counterfactuals.

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

pattc_counterfactuals function

<dl><dt>pop.data</dt>
<dd>population dataset</dd>
<dt>response.mod</dt>
<dd>trained model from <code>response_model</code>.</dd>
<dt>ID</dt>
<dd>string fir identifier variable</dd>
<dt>cluster</dt>
<dd>string for clustering variable</dd>
<dt>binary.outcome</dt>
<dd>logical specifying whether predicted outcomes are
proportions or binary (0-1).</dd></dl>

Arguments

Assess Population Data counterfactuals — pattc_counterfactuals

<dl>

<dt>pop.data</dt>
<dd>population dataset</dd>


<dt>response.mod</dt>
<dd>trained model from <code>response_model</code>.</dd>


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


<dt>cluster</dt>
<dd>string for clustering variable</dd>


<dt>binary.outcome</dt>
<dd>logical specifying whether predicted outcomes are
proportions or binary (0-1).</dd>

</dl>

pattc_counterfactuals: Assess Population Data counterfactuals

Description

Usage

Value

Arguments