Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes

This repository is the official implementation of Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes.

Required packages

• python == 3.9.13
• numpy == 1.24.3
• pandas == 1.5.3
• scipy == 1.10.1
• torch == 1.10.0
• tqdm == 4.65.0

To run the code

Run synthesize_dataset.py for generation of synthetic dataset of refugees. The generation results will be saved in a directory 'data/synthetic_100X5000_10_locations'.

python synthesize_dataset.py --refugee_batch_size [number of refugee in a batch] --refugee_batch_num [number of refugee batches]

Execute extract_classifier_scores.py to extract employment probability of each refugee predicted by biased classifier. The extracted employment probability will be saved in a directory 'save_dir/scores'

python extract_classifier_scores.py --beta [bias of the classifier]

Then, start our proposed postprocessing framework by computing minimally modified weight that is guaranteed to be counterfactually harmless. The adjusted weight will be saved in a directory 'save_dir/problems'

python create_problem.py --epsilon [epsilon used in the algorithm] --w [noise level of the default policy]

Train the deep learning model that learns to avoid counterfactual harm. The trained model and evaluation result will be saved in a directory 'result'

python train_modifier.py

Please see utils/common.py for other possible arguments.