TRESNET

This is the official implementation of our paper

@inproceedings{tec2023causal,
  title={Causal Estimation of Exposure Shifts with Neural Networks},
  author={Tec, Mauricio and Mudele, Oladimeji and Josey, Kevin and Dominici, Francesca},
  booktitle={KDD},
  year={2024}
}

Experiments

We use the Snakemake pipeline system for reproducibility.

The install the dependencies, run

conda env create -f requirements.yaml  # defines the tresnet conda env

The code to reproduce all experiments is in the Snakefile. At a high level, the code runs using the following command

source activate tresnet
snakemake --cores <num-cores> -C experiment=<exp> strategy=<strategy>

where <exp> is the experiment number (see the paper for experiment's definition), <strategy> is the causal estimation method. The config file conf/snakemake.yaml defines all the possible strategies per experiment.

Snakemake is running the main training script in main.py with the following pattern

python main.py
    dataset=<indp/news/...>
    strategy=<tresnet/aipw/...>
    outcome.backbone=<piecewise/vc>
    family=<gaussian/poisson/bernoulli>
    seed=<seed>

We use the Hydra configuration system to manage the hyperparameters. The conf/ folder contains the configuration files for each experiment. The conf/config.yaml file contains the default hyperparameters for the models.

The tresnet/ contains all the auxiliary code such as the network architectures and utilies. Additionally, the benchmark datasets are constructed in the module tresnet/datamodules/.

See the folder slurm/ folder for example scripts to run the experiments using snakemake. We used these scripts for the paper experiments in a cluster.