python_ready

Call this to manually set up Python and dependencies. The function checks if Python is available via the <code>reticulate</code> package, and if not, it creates a virtual environment and installs the specified Python modules.

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

python_ready function

<dl><dt>modules</dt>
<dd>Character vector of Python modules to check for and install if missing.</dd>
<dt>envname</dt>
<dd>Name of the virtual environment to use or create. Defaults to "r-reticulate".</dd></dl>

Arguments

Check for Python module availability and install if missing. — python_ready

<dl>

<dt>modules</dt>
<dd>Character vector of Python modules to check for and install if missing.</dd>


<dt>envname</dt>
<dd>Name of the virtual environment to use or create. Defaults to "r-reticulate".</dd>

</dl>

python_ready: Check for Python module availability and install if missing.

Description

Usage

Value

Arguments

Examples