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Codebase for "GANITE"

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.

To run the pipeline for training and evaluation on GANITE framwork, simply run python3 -m main_ganite.py.

Code explanation

(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.

Command inputs:

  • 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.

Example command

$ python3 main_ganite.py --data_name twin --train_rate 0.8 
--h_dim 30 --iteration 10000 --batch_size 256 --alpha 1

Outputs

  • test_y_hat: estimated potential outcomes
  • metric_results: PEHE and ATE