Timezone: »

 
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Martin Pawelczyk · Sascha Bielawski · Johan Van den Heuvel · Tobias Richter · Gjergji Kasneci

@
in Dataset and Benchmark Poster Session 3 »

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.

[ Chat] 

Author Information

Martin Pawelczyk (University of Tübingen)

# Academic Exp ## Phd Student at Uni of Tübingen, Germany: ## MSc Statistics, London School of Economics, UK ## MSc Econometrics, University of Edinburgh, UK ## BSc Economics, University of Cologne, Germany # Work Exp ## ML intern at SDG financing Lab, OECD, Paris ## Working student at r2b energy consulting, Cologne

Sascha Bielawski (University of Tuebingen)
Johan Van den Heuvel (University of Tuebingen)
Tobias Richter (University of Cologne)
Gjergji Kasneci (University of Tuebingen)

More from the Same Authors