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Offline Policy Evaluation: Counterfactual Estimator

This repo is to reproduce the result of Doubly Robust Estimator.

Dataset: ./data/*

Dataset Samples Features Labels
ecoli 336 7 8
glass 214 9 6
letter 20000 16 26
optdigits 5620 64 10
page_blocks 5473 10 5
pendigits 10992 16 10
satimage 6435 36 6
vehicle 846 18 4
yeast 1484 8 10

**All data can be found under UCI's great repositories

Results

  • Bias: ./results/result_bias.txt
  • RMSE: ./results/result_rmse.txt

Usage

  • If Anaconda, please create your env: conda create -n <ENV_NAME> python=3.6.8
  • Get dependencies: pip install -r requirements.txt
  • Run the main file: python main.py

Dependencies

  • Python: 3.6.8
  • Packages: See ./requirements.txt
  • OS: Windows10, MacOS, Ubuntu(18.04/16.04 LTS)

TODO

  • Fuse the evaluation metrics of OPE by Mix and Rank!!

Reference