Autoencoder-based propensity score

This repository is associated with the following study:

Weberpals, Janick; Becker, Tim; Davies, Jessica; Schmich, Fabian; Rüttinger, Dominik; Theis, Fabian J.; Bauer-Mehren, Anna. Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research, Epidemiology: May 2021 - Volume 32 - Issue 3 - p 378-388 doi: 10.1097/EDE.0000000000001338

The computing code used in this study is available as Python Jupyter Markdown scripts (.html) as supplementary material. All of the analyses described in the article were performed in R version 3.2.2. The PCA and autoencoder training was performed using sckit-learn and Keras with Tensorflow backend in Python version 3.6.0, respectively. The code that was used for the simulation is available as Rmarkdown.