CARLA: A Python Library to Benchmark Algorithmic Recourse and\n Counterfactual Explanation Algorithms

Abstract

Counterfactual explanations provide means for prescriptive model explanations\nby suggesting actionable feature changes (e.g., increase income) that allow\nindividuals to achieve favorable outcomes in the future (e.g., insurance\napproval). Choosing an appropriate method is a crucial aspect for meaningful\ncounterfactual explanations. As documented in recent reviews, there exists a\nquickly growing literature with available methods. Yet, in the absence of\nwidely available opensource implementations, the decision in favor of certain\nmodels is primarily based on what is readily available. Going forward - to\nguarantee meaningful comparisons across explanation methods - we present CARLA\n(Counterfactual And Recourse LibrAry), a python library for benchmarking\ncounterfactual explanation methods across both different data sets and\ndifferent machine learning models. In summary, our work provides the following\ncontributions: (i) an extensive benchmark of 11 popular counterfactual\nexplanation methods, (ii) a benchmarking framework for research on future\ncounterfactual explanation methods, and (iii) a standardized set of integrated\nevaluation measures and data sets for transparent and extensive comparisons of\nthese methods. We have open-sourced CARLA and our experimental results on\nGithub, making them available as competitive baselines. We welcome\ncontributions from other research groups and practitioners.\n

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