This repo contains the source-code of the paper: Counterfactual plans under distributional ambiguity.

This experiment evaluates MahalanobisCrr on different number of used corrections K and perturbation limits \Delta. To run this experiment:

python run_epts.py --ept 1 --datasets german sba student --classifiers lrt -uc --run-id <run-id>

The results will be saved in results/run_<run_id>/ept_1

This experiment investigates the impact of degree of distribution shifts on the validity of counterfactual plans. To run this experiment:

python run_epts.py --ept 3 --datasets synthesis --methods dice mahalanobis pgd --classifiers lrt -uc --run-id <run-id>

Results will be saved in results/run_<run_id>/ept_3

This experiment compares three methods DiCE, MahalanobisCrr, DroDicePGD in the three real world datasets: german, sba, student

1. First, prepare an underlying classifier and 'future' classifiers and for each dataset:

python run_epts.py --ept pretrain --datasets german german_shift sba sba_shift student student_shift --classifiers lrt --run-id 0
mv results/run_0/ept_pretrain/*.pickle data/pretrain 

2. Next, run the experiment:

python run_epts.py --ept 2 --classifiers lrt --datasets german sba student --methods dice mahalanobis pgd --run-id <run-id> --num-proc 32

3. The results will be saved in results/run_<run_id>/ept_2