CausalEGM
Estimating Causal Effect by Deep Encoding Generative Modeling. CausalEGM utilizes deep generative neural newtworks for estimating the causal effect by decoupling the high-dimensional confounder into a set of different latent variables with specific dependency on treatment or potential outcome.
Requirements
- TensorFlow>=2.4.1
- Python>=3.6.1
Install
CausalEGM can be installed by
pip install causalEGM
Software has been tested on a Linux (Centos 7) with Python3.9. A GPU card is recommended for accelerating the training process.
Reproduction
This section provides instructions on how to reproduce results in the our paper.
Simulation data
We tested CausalEGM with simulation datasets first.