DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks

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.

Getting started

Package details

AuthorNguyen K. Huynh [aut, cre] (<>), Bumba Mukherjee [aut] (<>), Irvin (Chen-Yu) Lee [aut] (<>)
MaintainerNguyen K. Huynh <>
Package repositoryView on CRAN
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DeepLearningCausal documentation built on July 3, 2024, 5:09 p.m.