Causal Inference with Super Learner and Deep Neural Networks
Train complier model using ensemble methods
Complier model prediction
Create list for experimental data
metalearner_deepneural
metalearner_ensemble
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
Estimate PATT_C using Deep NN
PATT_C SL Ensemble
Create list for population data
print.metalearner_deepneural
print.metalearner_ensemble
print.pattc_deepneural
print.pattc_ensemble
Response model from experimental data using SL ensemble
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 Künzel et al. (2019) <doi:10.1073/pnas.1804597116> 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) <doi:10.1515/jci-2018-0035> PATT-C estimator to obtain the PATT from experimental data with noncompliance by using the SL ensemble method and deep neural networks.
Useful links