The R-package DNNcausal implements estimators of average causal effect and average causal effect on the treated, combining AIPW with deep neural networks fits of nuisance functions. A possible use of the package is to use one-dimensional convolutional neural networks for time-series data inputs (first introduced in Ghasempour et al, 2023). Tensorflow and Keras are used to implement the neural network models.
Examples of use can be found at Causal_CNN
Ghasempour, M, Moosavi, N, de Luna, X. (2023). Convolutional neural networks for valid and efficient causal inference. Journal of Computational and Graphical Statistics. Open access: DOI: 10.1080/10618600.2023.2257247. On arXiv: https://doi.org/10.48550/arXiv.2301.11732
To install and load this package in R from GitHub, run the following commands:
install.packages("devtools")
library(devtools)
install_github("stat4reg/DNNcausal")
library(DNNcausal)
keras::install_keras()
Mac users might have to run the following command in terminal:
$ xcode-select --install
