Guide to Simulating Economic Datasets using WGANs
This repository contains
wgan, a python module built on PyTorch for using WGANs to simulate from joint and conditional distributions of economic datasets. Brief documentation and the API for the module is available here.
This Google Colab Notebook contains a tutorial for the code, including how to install requirements, and estimate and simulate from the resulting models, using a free Google GPU. The tutorial simulates from the Lalonde-Dehejia-Wahba dataset, as described in detail in the following paper.
Athey, Susan, Guido Imbens, Jonas Metzger, and Evan Munro. "Using Wasserstein Generative Adversial Networks for the Design of Monte Carlo Simulations." arXiv:1909.02210. September 2019.
The data folder contains the raw Lalonde-Dehejia-Wahba data.
For running the WGAN code outside of a Google Colab environment an installation of python3 is required.
In order to install this package dependencies, please use requirements.txt:
git clone https://www.github.com/gsbDBI/ds-wgan cd ds-wgan pip install -r requirements.txt
In addition, in order to install the package itself, you can run:
python setup.py develop