complier_mod

Train model using group exposed to treatment with compliance as binary
outcome variable and covariates.

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

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 identifier variable.</dd>
<dt>SL.learners</dt>
<dd>vector of strings for ML classifier algorithms. Defaults to
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 identifier variable.</dd>


<dt>SL.learners</dt>
<dd>vector of strings for ML classifier algorithms. Defaults to
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