CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
This work addresses the need for transparent and reproducible comparisons in algorithmic recourse and counterfactual explanations for researchers and practitioners, though it is incremental as it builds on existing methods.
The authors tackled the problem of comparing counterfactual explanation methods by developing CARLA, a Python library that benchmarks 11 popular methods across datasets and models, providing open-source implementations and standardized evaluation measures.
Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favorable 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 opensource implementations, the decision in favor of certain models is primarily based on what is readily available. Going forward - to guarantee meaningful comparisons across explanation methods - we present CARLA (Counterfactual And Recourse LibrAry), 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 CARLA and our experimental results on Github, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.