This repository is the official implementation of the paper "Deeply-Debiased Off-Policy Interval Estimation" (ICML 2021) in Python.

Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy. In addition to a point estimate, many applications would benefit significantly from having a confidence interval (CI) that quantifies the uncertainty of the point estimate. In this paper, we propose a novel procedure to construct an efficient, robust, and flexible CI on a target policy's value. Our method is justified by theoretical results and numerical experiments.

Method	Results

1. Code files in the main folder:
   1. Methods:
      1. _TRIPLE.py: main function for the proposed method
      2. _IS.py: code to implement the two IS-based competing methods
   2. Environment:
      1. _Ohio_Simulator.py: simulate for the Diabates environment
      2. _cartpole.py: simulate for the Cartpole environment, forked from OpenAI Gym, with slight modifications.
   3. _util.py: helper functions
   4. _analyze.py: post-process simulation results
2. /density: functions for estimating the two density ratio functions
3. /coinDice: code for the competing method "coinDice". Forked from https://github.com/google-research/dice_rl
4. /target_policies: checkpoints for the learned target policies
5. /RL: some useful RL functions
   1. DQN.py and FQI.py: implementation of the target/behaviour policies
   2. FQE.py: function for estimating the initial Q function
   3. my_gym.py: helper functions for training
   4. sampler.py: samplers and replay buffers
6. /TOY: code to generate the two plots for toy examples
   1. TOY_coverage.ipynb: for the plot showing the CI coverage
   2. TOY_TRIPLY.ipynb: for the plot showing the triply robust property
   3. _plot.py: helper functions for plotting
   4. _discrete.py: TR method for discrete state space
7. /script: scripts to run the experiments.

To reproduce our simulation experiment results, please follow the steps:

1. install the required packages
2. change the working directory to the main folder
3. open the jupyer notebook and modify the hyper-parameters
4. run and analyze the output results