Robust Counterfactual Explanations

This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations".

Installation

Run pip install . to install this repo. To install the search algorithms CoGS, LORE, and Growing Spheres, do:

cd methods; for m in *; do cd $m; pip install .; cd ../; done; cd ../;

Running

The file run.py can be used to search for counterfactual examples $\mathbf{z}$ in a dataset. For example,

python run.py --method cogs --dataset garments --check_plausibility 1 --optimize_C_robust 0 --optimize_K_robust 0 --n_jobs 16 --n_reps 5

will use CoGS to search for counterfactual examples in the data set Garments (called Productivity in the paper) while respecting the plausibility constraints that are defined for that data set.

Reproducing the results of the paper

The notebook to reproduce all figures and tables in the paper is notebooks/reproduce.ipynb. Furthermore, some meta-info on the data sets and annotations is provided in notebooks/dataset_info.ipynb. Note that the logs in the folder results require GIT-LFS to be obtained.

Including your own counterfactual search algorithm

If you with to include your own counterfactual search algorithm, you must create a wrapper for it. This can be done by editing the file robust_cfe/wrappers.py similarly to how we implemented wrappers for CoGS, LORE, GrowingSpheres, and SciPy's Nelder-Mead.

Creating your own data set

Upload the data of interest in the folder datasets. Next, edit the file robust_cfe/dataproc.py to include the pre-processing for your data set, which should be similar to the one we provide for the existing data sets. If you wish, you can define plausibility constraints and possible perturbations, like we did.