Partial Counterfactual Identification

Partial counterfactual identification for continuous outcomes with a Curvature Sensitivity Model

The project is built with the following Python libraries:

1. Pyro - deep learning and probabilistic modelling with residual normalizing flows and variational augmentations
2. Hydra - simplified command line arguments management
3. MlFlow - experiments tracking
4. POT - pytorch implementation of the Wasserstein distance
5. normflows - pytorch implementation of the residual normalizing flows

Installations

First one needs to make the virtual environment and install all the requirements:

pip3 install virtualenv
python3 -m virtualenv -p python3 --always-copy venv
source venv/bin/activate
pip3 install -r requirements.txt

MlFlow Setup / Connection

To start an experiments server, run:

mlflow server --port=5000

To access MlFLow web UI with all the experiments, connect via ssh:

ssh -N -f -L localhost:5000:localhost:5000 <username>@<server-link>

Then, one can go to local browser http://localhost:5000.

Real-world case study

We use multi-country data from Banholzer et al. (2021). Access data here.

Experiments

Main training script of Augmented Pseudo-Invertible Decoder (APID) is universal for different datasets. For details on mandatory arguments - see the main configuration file config/config.yaml and other files in configs/ folder.

Generic script with logging and fixed random seed is following:

PYTHONPATH=.  python3 runnables/train_apid.py +dataset=<dataset> +model=apid exp.seed=10 exp.logging=True

Example of running APID on multi-modal dataset with curvature loss coefficient $\lambda_\kappa = 5.0$ and factual outcomes $y' = 0.0,0.5,1.0,1.5,2.0$:

PYTHONPATH=.  python3 runnables/train_apid.py +dataset=multi_modal +model=apid exp.seed=10 exp.logging=True model.curv_coeff=5.0 dataset.Y_f=0.0,0.5,1.0,1.5,2.0

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