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DeepLearningCausal  

Causal Inference with Super Learner and Deep Neural Networks
View on CRAN: Click here


Download and install DeepLearningCausal package within the R console
Install from CRAN:
install.packages("DeepLearningCausal")

Install from Github:
library("remotes")
install_github("cran/DeepLearningCausal")

Install by package version:
library("remotes")
install_version("DeepLearningCausal", "0.0.104")



Attach the package and use:
library("DeepLearningCausal")
Maintained by
Nguyen K. Huynh
[Scholar Profile | Author Map]
First Published: 2024-06-18
Latest Update: 2024-06-18
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 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.
How to cite:
Nguyen K. Huynh (2024). DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks. R package version 0.0.104, https://cran.r-project.org/web/packages/DeepLearningCausal. Accessed 02 Apr. 2025.
Previous versions and publish date:
0.0.102 (2024-06-18 16:40), 0.0.103 (2024-07-01 17:10)
Other packages that cited DeepLearningCausal R package
View DeepLearningCausal citation profile
Other R packages that DeepLearningCausal depends, imports, suggests or enhances
Complete documentation for DeepLearningCausal
Functions, R codes and Examples using the DeepLearningCausal R package
Full DeepLearningCausal package functions and examples
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