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NOFLITE: Learning to Predict Individual Treatment Effect Distributions

This repository provides the code for the paper "NOFLITE: Learning to Predict Individual Treatment Effect Distributions" (TMLR 2023).

The structure of the code is as follows:

NOFLITE/
|_ benchmarks/      # Folder containing the benchmark methodologies (CMGP is provided through its own package; BART was ran in R)
  |_ ganite/        
  |_ cevae.py        
  |_ fccn.py        
|_ datasets/        # Folder that should contain the datasets; the paper's appendix provides information where to obtain this data
LICENSE             # MIT License
data_loading.py     # Code to load the data
gaussianization.py  # Code for Gaussianization flow
lossfunctions.py    # Functions for the MMD loss
main.py             # Main executable to run experiment
metrics.py          # Evaluation metrics
noflite.py          # Code for NOFLITE
sigmoid_flow.py     # Code for Sigmoidal Flow
visualize.py        # Code for creating the visualizations

Installation

The requirements.txt provides the necessary packages. All code was written for python 3.9. Weights and Biases (W&B) is required to log the experiments.

Usage

The experiments can be run through the main.pyfile using the following arguments:

python main.py
[--lr [learning rate]]
[--lambda_l1 [l1 regularization]]
[--lambda_l2 [l2 regularization]]
[--batch_size [batch size]]
[--noise_reg_x [x noise regularization]]
[--noise_reg_y [y noise regularization]]
[--hidden_neurons_encoder [hidden neurons in NOFLITE's encoder]]
[--hidden_layers_balancer [hidden layers in NOFLITE's balancer]]
[--hidden_layers_encoder [hidden layers in NOFLITE's encoder]]
[--hidden_layers_prior [hidden layers in NOFLITE's prior]]
[--hidden_neurons_trans [hidden neurons in the flow's transformer]]
[--hidden_neurons_cond [hidden neurons in the flow's conditioner]]
[--hidden_layers_cond [hidden layers in the flow's conditioner]]
[--dense [dense version of the flow]]
[--n_flows [number of flow transformations]]
[--datapoint_num [kernel size for Gaussianization flow]]
[--resid_layers [residual layers for Residual/RQNSF-AR flow]]
[--max_steps [training steps]]
[--flow_type [type of flow transformation]]
[--metalearner [type of metalearner]]
[--lambda_mmd [strength of the MMD regularization]]
[--n_samples [number of samples]]
[--trunc_prob [truncation probability]]
[--dataset [dataset to use for the experiment]]
[--bin_outcome [whether to use a binary outcome]]
[--iterations [number of experiment iterations]]
[--visualize [whether to visualize the model's output]]
[--sweep [whether to perform a hyperparameter sweep]]
[--wandb [whether to use wandb]]
[--NOFLITE [whether to include NOFLITE in the experiment]]
[--CMGP [whether to include CMGP in the experiment]]
[--CEVAE [whether to include CEVAE in the experiment]]
[--GANITE [whether to include GANITE in the experiment]]
[--FCCN [whether to include FCCN in the experiment]]

Example usage to train NOFLITE on IHDP:

python main.py --dataset=IHDP --iterations=100 --n_samples=500 --metalearner=T --flow_type=SigmoidX --batch_size=128 --hidden_neurons_encoder=8 --hidden_layers_balancer=2 --hidden_layers_encoder=3 --hidden_layers_prior=2 --lr=5e-4 --max_steps=5000 --n_flows=0 --lambda_mmd=1 --lambda_l1=1e-3 --lambda_l2=5e-4 --noise_reg_x=0.0 --noise_reg_y=5e-1 --trunc_prob=0.0 --no-sweep --no-wandb --no-visualize --NOFLITE --no-CMGP --no-CEVAE --no-GANITE --no-FCCN

Acknowledgements

We build upon code provided by the authors of CCN (Zhou et al., 2023).

Zhou, Tianhui, William E. Carson IV, and David Carlson. "Estimating Potential Outcome Distributions with Collaborating Causal Networks." Transactions on Machine Learning Research (2022).

Citing

Please cite our paper and/or code as follows:

@InProceedings{noflite2023,
  title = 	 {{NOFLITE: Learning to Predict Individual Treatment Effect Distributions}},
  author =       {Vanderschueren, Toon and Berrevoets, Jeroen and Verbeke, Wouter},
  booktitle = 	 {Transactions on Machine Learning Research},
  year = 	 {2023},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  publisher =    {PMLR},
  url =      {https://openreview.net/forum?id=q1Fey9feu7},
}