Original PyTorch implementation of Causal-Graphical Normalizing Flows demonstrating the use of c-GNF on simulated datasets.

The implementation of c-GNF is done by extending the offical codes for the paper: Graphical Normalizing Flows, Antoine Wehenkel and Gilles Louppe. (May 2020). [arxiv] [github]

Balgi, S., Peña, J. M., & Daoud, A. (2022). Personalized Public Policy Analysis in Social Sciences Using Causal-Graphical Normalizing Flows. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 11810-11818. [paper]

The list of dependencies can be found in requirements.txt text file and installed with the following command:

pip install -r requirements.txt

This repository provides some code to build diverse types normalizing flow models in PyTorch. The core components are located in the models folder. The different flow models are described in the file NormalizingFlow.py and they all follow the structure of the parent class NormalizingFlow. A flow step is usually designed as a combination of a normalizer (such as the ones described in Normalizers sub-folder) with a conditioner (such as the ones described in Conditioners sub-folder). Following the code hierarchy provided makes the implementation of new conditioners, normalizers or even complete flow architecture very easy.

Refer cGNF_Wodtke_simulated_experiments_s1.ipynb jupyter notebook for further details on simulated experiments with no model misspecification and no data heterogeneity setting !!

Refer cGNF_Wodtke_simulated_experiments_s2.ipynb jupyter notebook for further details on simulated experiments with only outcome model misspecification and data heterogeneity setting !!

Refer cGNF_Wodtke_simulated_experiments_s3.ipynb jupyter notebook for further details on simulated experiments with both treatment and outcome model misspecification and data heterogeneity setting !!