deep_predict

Predict Compliance from control group in experimental data

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

deep_predict function

<dl><dt>deep.complier.mod</dt>
<dd>model object from <code>deep.complier.mod()</code></dd>
<dt>complier.formula</dt>
<dd>formula to fit compliance model (c ~ x) using
complier variable and covariates</dd>
<dt>exp.data</dt>
<dd><code>data.frame</code> object of experimental dataset</dd>
<dt>treat.var</dt>
<dd>string specifying the binary treatment variable</dd>
<dt>compl.var</dt>
<dd>string specifying binary complier variable</dd></dl>

Arguments

Complier model prediction — deep_predict

<dl>

<dt>deep.complier.mod</dt>
<dd>model object from <code>deep.complier.mod()</code></dd>


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


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


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


<dt>compl.var</dt>
<dd>string specifying binary complier variable</dd>

</dl>

deep_predict: Complier model prediction

Description

Usage

Value

Arguments