Part of Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021) round1
Martin Pawelczyk, Sascha Bielawski, Johan Van den Heuvel, Tobias Richter, Gjergji. Kasneci
Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favourable outcomes in the future (e.g., insurance approval).Choosing an appropriate method is a crucial aspect for meaningful counterfactual explanations. As documented in recent reviews, there exists a quickly growing literature with available methods. Yet, in the absence of widely available open--source implementations, the decision in favour of certain models is primarily based on what is readily available. Going forward -- to guarantee meaningful comparisons across explanation methods -- we present \texttt{CARLA} (\textbf{C}ounterfactual \textbf{A}nd \textbf{R}ecourse \textbf{L}ibr\textbf{A}ry), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods.We have open sourced \texttt{CARLA} and our experimental results on \href{https://github.com/indyfree/CARLA}{Github}, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.