MIRACLE (Missing data Imputation Refinement And Causal LEarning)

Code Author: Trent Kyono

This repository contains the code used for the "MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms" paper(2021).

Installation

pip install -r requirements.txt
pip install .

CUDA support (optional)

For CUDA support, you should make sure TensorFlow 2 CUDA requirements are met.

Install the library as above, then install the CUDA enabled version of TensorFlow by following the official instructions.

Tests

You can run the tests using

pip install -r requirements_dev.txt
pip install .
pytest -vsx

Contents

• miracle/MIRACLE.py - Imputer/Refiner Class. This class takes a baseline imputation and returns a refined imputation. This code has been forked from [2].
• miracle/third_party - Reference imputers: Mean, Missforest, MICE, GAIN, Sinkhorn, KNN.
• tests/run_example.py - runs a nonlinear toy DAG example. Uses mean imputation as a baseline and applies MIRACLE to refine.

Examples

Base example on toy dag.

$ cd tests/
$ python run_example.py

This specific instantiation returns a Baseline RMSE of approximately 0.95 with MIRACLE RMSE of approximately 0.40.

An example to run toy example with a dataset size of 2000 for 300 max_steps with a missingness of 30%

$ python3 run_example.py --dataset_sz 2000 --max_steps 300 --missingness 0.3

Citing

@inproceedings{kyono2021miracle,
	title        = {MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms},
	author       = {Kyono, Trent and Zhang, Yao and Bellot, Alexis and van der Schaar, Mihaela},
	year         = 2021,
	booktitle    = {Conference on Neural Information Processing Systems(NeurIPS) 2021}
}

References

[1] Jinsung Yoon, James Jordon, and Mihaela van der Schaar. Gain: Missing data imputation using generative adversarial nets. In ICML, 2018.

[2] Trent Kyono, Yao Zhang, and Mihaela van der Schaar. CASTLE: Regularization via auxiliary causal graph discovery. In NeurIPS, 2020.

[3] Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning (NeurIPS 2018).

[4] Zheng, X., Dan, C., Aragam, B., Ravikumar, P., & Xing, E. P. (2020). Learning sparse nonparametric DAGs (AISTATS 2020).