Simple Steps to Success: A Method for Step-Based Counterfactual Explanations
Abstract: Algorithmic recourse is a process that leverages counterfactual explanations, going beyond understanding why a system produced a given classification, to providing a user with actions they can take to change their predicted outcome. Existing approaches to compute such interventions---known as {\em recourse}---identify a set of points that satisfy some desiderata---e.g. an intervention in the underlying causal graph, minimizing a cost function, etc. Satisfying these criteria, however, requires extensive knowledge of the underlying model structure, an often unrealistic amount of information in several domains. We propose a data-driven and model-agnostic framework to compute counterfactual explanations. We introduce StEP, a computationally efficient method that offers \emph{incremental steps} along the data manifold that directs users towards their desired outcome. We show that StEP uniquely satisfies a desirable set of axioms. Furthermore, via a thorough empirical and theoretical investigation, we show that StEP offers provable robustness and privacy guarantees while outperforming popular methods along important metrics.
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Stefano_Teso1
Submission Number: 2886
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