Simple Steps to Success: A Method for Step-Based Counterfactual Explanations

• URL: http://arxiv.org/abs/2306.15557v3
• Date: Tue, 12 Nov 2024 21:04:32 GMT
• Title: Simple Steps to Success: A Method for Step-Based Counterfactual Explanations
• Authors: Jenny Hamer, Nicholas Perello, Jake Valladares, Vignesh Viswanathan, Yair Zick,
• Abstract summary: We propose a data-driven and model-agnostic framework to compute counterfactual explanations. We introduce StEP, a computationally efficient method that offers incremental steps along the data manifold that directs users towards their desired outcome.
• Score: 9.269923473051138
• License:
• 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 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 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.

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