Initial code release

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LICENSE

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+MIT License
+
+Copyright (c) 2023 Shengpu Tang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation
+
+This repository contains the source code for replicating all experiments in the NeurIPS 2023 paper, "Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation".
+
+Repository content:
+
+- `synthetic/` contains code for the experiments on the toy bandit problems. 
+- `sepsisSim/` contains code for the experiments on the sepsis simulator.
+
+If you use this code in your research, please cite the following publication:
+```
+@inproceedings{tang2023counterfactual,
+    title={Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation},
+    author={Tang, Shengpu and Wiens, Jenna},
+    booktitle={Advances in Neural Information Processing Systems},
+    year={2023},
+    url={https://openreview.net/forum?id=dsH244r9fA}
+}
+```
+
+## Synthetic domains - Bandits (Sec 5.1 & Appx E.1)
+- `bandit_compare-2state.ipynb`: Table 1, Appx E Table 3
+- `bandit_compare-1state.ipynb`: Appx E Table 4
+- `bandit_sweepW.ipynb`: Appx E Fig 7 (varying weights)
+- `bandit_sweepPcannot.ipynb`: Appx E Fig 8 (varying percent annotated and imputation)
+
+## Healthcare domain - Sepsis simulator (Sec 5.2 & Appx E.2)
+- Simulator based on publicly available code at https://github.com/clinicalml/gumbel-max-scm/tree/sim-v2
+- Experiment code structure are inspired by https://github.com/MLD3/OfflineRL_ModelSelection and https://github.com/MLD3/OfflineRL_FactoredActions
+- The preparation steps are in `data-prep/`, which include the simulator source code as well as several notebooks for dataset generation. The output is saved to `data/` (ground-truth MDP parameters, ground-truth optimal policy, and optimal value functions) and `datagen/` (offline datasets). 
+- The code for the main experiments is in `experiments/`. 
+    - `0-prepopulate_annotations.ipynb` preprocesses the data so that all counterfactual annotations are pre-populated. This step is run only once (instead of repeated for each experiment) as it takes a long time. The annotations are later replaced with different values or removed depending on the experiment. The output is saved to `results/vaso_eps_0_1-annotOpt_df_seed2_aug_step.pkl`.
+    - Use `commands.sh` to run the experiments.
+    - Run the following notebooks to generate tables and figures used in the paper:
+        - `results.ipynb`: Table 2, Appx E Table 5 top, Appx E Fig 9
+        - `results-OIS-WIS.ipynb`: Appx E Table 5 bottom
+        - `plots-analyses.ipynb`: Fig 5 left, Appx E Fig 10
+        - `plots-noisy[-v2].ipynb`: Fig 5 center, Appx E Fig 11 left&center
+        - `plots-missing.ipynb`: Fig 5 right, Appx E Fig 11 right
+        - `plots--legend.ipynb`: figure legend used in Fig 5 and Fig 11
+    - `fig/` contains the main figures used in the paper.