Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar
Reference: Jinsung Yoon, James Jordon, Mihaela van der Schaar, "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets", International Conference on Learning Representations (ICLR), 2018.
Paper link: https://openreview.net/forum?id=ByKWUeWA-
Contact: [email protected]
This directory contains implementations of GANITE framework for individualized treatment effect estimations using a real-world dataset.
- Twin data: http:https://data.nber.org/data/linked-birth-infant-death-data-vital-statistics-data.html
To run the pipeline for training and evaluation on GANITE framwork, simply run python3 -m main_ganite.py.
(1) data_loading.py
- Transform raw twins data to preprocessed ITE data (X, T, Y, Potential Y)
(2) metrics.py (a) PEHE
- Precision in Estimation of Heterogeneous Effect (b) ATE
- Average Treatment Effect
(3) ganite.py
- Use observed features, treatments and factual outcomes to estimate the potential outcomes
(4) main_ganite.py
- Report PEHE and ATI for the twin dataset with GANITE
(5) utils.py
- Some utility functions for GANITE.
- data_name: twin
- train_rate: the ratio of training data
- h_dim: hidden dimensions
- iterations: number of training iterations
- batch_size: the number of samples in each batch
- alpha: hyper-parameter to adjust the loss importance
Note that network parameters should be optimized.
$ python3 main_ganite.py --data_name twin --train_rate 0.8
--h_dim 30 --iteration 10000 --batch_size 256 --alpha 1
- test_y_hat: estimated potential outcomes
- metric_results: PEHE and ATE