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AutoIV

Introduction

This repository contains the implementation code for paper:

Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition

Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, Lanfen Lin

ACM Transactions on Knowledge Discovery from Data (TKDD), 2022

[arXiv]

Brief Abstract for the Paper


Instrumental Variable (IV) is a powerful tool for causal inference, but it is hard to find/pre-define valid IVs. We propose an Automatic Instrumental Variable decomposition (AutoIV) algorithm to generate IV representations from observed variables through mutual information constraints for IV-based counterfactual prediction.

Requirements

You may need to build suitable Python environment for the experiments.

The following package versions are recommened.

  • python 3.6
  • tensorflow-gpu 1.15.0

Device:

  • GPU with VRAM > 3GB (strictly).
  • Memory > 4GB.

Usage

  1. Configure run.sh file.
  2. Run the code with command:
nohup sh run.sh > run.txt 2>&1 &
  1. Your may check the results in the following path:
Information Path to check Note
Generated synthetic data data/dataset/dataset-train_data_num/ x: treatment; y: structural response; ye: true outcome.
Generated IV representation data/dataset/autoiv-dataset/autoiv-dataset-train_data_num-rep_rep_dim/data/ col 1 (x): treatment;
col 2 (x_pre): treatment predicted by treatment network;
col 3 (y): true outcome;
col 4 (y_pre): outcome predicted by outcome network;
col 5~{4+rep_dim}: IV representation Z;
col {5+rep_dim}~{4+2*rep_dim}: confounder representation C.
Training details of AutoIV AutoIV-results/dataset-train_data_num-date/ Trace of Loss and MSE error of training, validation, and test data during training.

Updates

Citation

If you find our code or idea useful for your research, please consider citing our work.

@article{yuan2022auto,
  title={Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition},
  author={Yuan, Junkun and Wu, Anpeng and Kuang, Kun and Li, Bo and Wu, Runze and Wu, Fei and Lin, Lanfen},
  journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
  volume={16},
  number={4},
  pages={1--20},
  year={2022},
  publisher={ACM New York, NY}
}

Contact

If you have any questions, feel free to contact us through email (yuanjk@zju.edu.cn or anpwu@zju.edu.cn) or GitHub issues. Thanks!