expcall

create list object of experimental data for easy data processing

internal

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

expcall function

<dl><dt>response.formula</dt>
<dd>formula for response equation of binary outcome
variable and covariates</dd>
<dt>treat.var</dt>
<dd>string for binary treatment variable</dd>
<dt>compl.var</dt>
<dd>string for complier variable</dd>
<dt>exp.data</dt>
<dd><code>data.frame</code> of experimental variable</dd>
<dt>weights</dt>
<dd>observation weights</dd>
<dt>cluster</dt>
<dd>clustering variable</dd>
<dt>ID</dt>
<dd>identifier variable</dd></dl>

Arguments

Create list for experimental data — expcall

<dl>

<dt>response.formula</dt>
<dd>formula for response equation of binary outcome
variable and covariates</dd>


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


<dt>compl.var</dt>
<dd>string for complier variable</dd>


<dt>exp.data</dt>
<dd><code>data.frame</code> of experimental variable</dd>


<dt>weights</dt>
<dd>observation weights</dd>


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


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

</dl>

expcall: Create list for experimental data

Description

Usage

Value

Arguments