Representation-Induced Counfounding Bias

Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation

The project is built with the following Python libraries:

1. Pyro - deep learning and probabilistic models (MDNs, NFs)
2. Hydra - simplified command line arguments management
3. MlFlow - experiments tracking

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 --gunicorn-opts "--timeout 280"

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 the local browser http://localhost:5000.

Experiments

The main training script is universal for different methods and 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 the following:

PYTHONPATH=.  python3 runnables/train.py +dataset=<dataset> +repr_net=<model> exp.seed=10

Representation learning methods for CATE (baselines)

Stage 0.

One needs to choose a model and then fill in the specific hyperparameters (they are left blank in the configs):

• TARNet: +repr_net=tarnet
• BNN: +repr_net=bnnet
• CFR: +repr_net=cfrnet
• InvTARNet: +repr_net=inv_tarnet
• RCFR: +repr_net=rcfrnet
• CFR-ISW: +repr_net=cfrisw
• BWCFR: +repr_net=bwcfr

Models already have the best hyperparameters saved, for each model - dataset and different sizes of the representation. One can access them via: +repr_net/<dataset>_hparams/<model>=<dim_repr_multiplier> or +model/<dataset>_hparams/<model>/<ipm_params>=<dim_repr_multiplier> etc. To perform manual hyperparameter tuning use the flags repr_net.tune_hparams=True, and then see repr_net.hparams_grid.

Stage 1.

Stage 1 models are propensity nets (src/models/prop_nets.py) and the conditional normalizing flow (src/models/msm.py). The hyperparameters were tuned together with the stage 0 models and are stored in the same YAML files. To perform manual hyperparameter tuning use the flags prop_net_cov.tune_hparams=True, prop_net_repr.tune_hparams=True and cnf_repr.tune_hparams=True.

Stage 2.

The bounds on the representation-induced confounding bias can be then estimated with the methods calculate_gammas and get_bounds from src/models/msm.py.

Datasets

Before running semi-synthetic experiments, place datasets in the corresponding folders:

• IHDP100 dataset: ihdp_npci_1-100.test.npz and ihdp_npci_1-100.train.npz to data/ihdp100/

One needs to specify a dataset / dataset generator (and some additional parameters, e.g. train size for the synthetic data dataset.n_samples_train=1000):

• Synthetic data (adapted from https://arxiv.org/abs/1810.02894): +dataset=synthetic
• IHDP dataset: +dataset=ihdp100
• HC-MNIST dataset: +dataset=hcmnist

Examples

Example of running TARNet without tuning based on synthetic data with n_train = 1000:

CUDA_VISIBLE_DEVICES=<devices> PYTHONPATH=. python3 runnables/train.py -m +dataset=synthetic +repr_net=tarnet +repr_net/synthetic_hparams/tarnet/n1000='0.5' exp.logging=True exp.device=cuda exp.seed=10

Example of all-stages tuning of CFR based on the 0-th subset of IHDP100 dataset with Wasserstein metric and $\alpha = 1.0$:

CUDA_VISIBLE_DEVICES=<devices> PYTHONPATH=. -m +dataset=ihdp100 +repr_net=cfrnet exp.logging=True exp.device=cuda dataset.dataset_ix=0 repr_net.ipm=wass repr_net.alpha=1.0 repr_net.tune_hparams=True prop_net_cov.tune_hparams=True prop_net_repr.tune_hparams=True cnf_repr.tune_hparams=True

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