Causal Inference with Super Learner and Deep Neural Networks
Build Keras model
Check for required CRAN packages and prompt installation if missing.
Check for required Python modules and prompt installation if missing.
Train complier model using ensemble methods
Complier model prediction
conformal_plot
Train complier model using deep neural learning through Tensorflow
Complier model prediction
Response model from experimental data using deep neural learning throu...
Create list for experimental data
hte_plot
metalearner_deeplearning
metalearner_ensemble
metalearner_neural
Train compliance model using neural networks
Assess Population Data counterfactuals
Predicting Compliance from experimental data
Modeling Responses from experimental data Using Deep NN
Assess Population Data counterfactuals
Assess Population Data counterfactuals
Deep PATT-C
PATT-C SL Ensemble
Estimate PATT_C using Deep NN
plot.metalearner_ensemble
plot.metalearner_neural
plot.pattc_deeplearning
plot.pattc_ensemble
plot.pattc_neural
Create list for population data
print.metalearner_deeplearning
print.metalearner_ensemble
print.metalearner_neural
print.pattc_deeplearning
print.pattc_ensemble
print.pattc_neural
Check for Python module availability and install if missing.
Response model from experimental data using SL ensemble
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.
Useful links