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DeepLearningCausal (version 0.0.106)

Causal Inference with Super Learner and Deep Neural Networks

Description

Functions to estimate Conditional Average Treatment Effects (CATE) and Population Average Treatment Effects on the Treated (PATT) from experimental or observational data using the Super Learner (SL) ensemble method and Deep neural networks. The package first provides functions to implement meta-learners such as the Single-learner (S-learner) and Two-learner (T-learner) described in KC for estimating the CATE. The S- and T-learner are each estimated using the SL ensemble method and deep neural networks. It then provides functions to implement the Ottoboni and Poulos (2020) PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.

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install.packages('DeepLearningCausal')

Monthly Downloads

281

Version

0.0.106

License

GPL-3

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Maintainer

Nguyen K. Huynh

Last Published

June 11th, 2025

Functions in DeepLearningCausal (0.0.106)

pop_data

World Value Survey India Sample
print.metalearner_deepneural

print.metalearner_deepneural
print.metalearner_ensemble

print.metalearner_ensemble
pop_data_full

World Value Survey India Sample
popcall

Create list for population data
response_model

Response model from experimental data using SL ensemble
print.pattc_deepneural

print.pattc_deepneural
print.pattc_ensemble

print.pattc_ensemble
complier_mod

Train complier model using ensemble methods
exp_data

Survey Experiment of Support for Populist Policy
complier_predict

Complier model prediction
metalearner_deepneural

metalearner_deepneural
metalearner_ensemble

metalearner_ensemble
neuralnet_complier_mod

Train compliance model using neural networks
neuralnet_pattc_counterfactuals

Assess Population Data counterfactuals
hte_plot

hte_plot
neuralnet_predict

Predicting Compliance from experimental data
plot.pattc_deepneural

plot.pattc_deepneural
neuralnet_response_model

Modeling Responses from experimental data Using Deep NN
pattc_counterfactuals

Assess Population Data counterfactuals
plot.pattc_ensemble

plot.pattc_ensemble
plot.metalearner_ensemble

plot.metalearner_ensemble
pattc_deepneural

Estimate PATT_C using Deep NN
pattc_ensemble

PATT_C SL Ensemble
plot.metalearner_deepneural

plot.metalearner_deepneural
expcall

Create list for experimental data
exp_data_full

Survey Experiment of Support for Populist Policy