pattc_counterfactuals

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

Functions for deep learning estimation of Conditional Average Treatment Effects (CATEs)
from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with
treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements
the conformal prediction framework that enables computation and illustration of conformal prediction (CP)
intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional
functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with
treatment noncompliance using weighted ensemble learning via the super learner approach and R 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.preds</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.preds</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