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arXiv · 2410.14522

Rethinking Distance Metrics for Counterfactual Explainability

Joshua Nathaniel Williams·Anurag Katakkar·Hoda Heidari·J. Zico Kolter
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Abstract

Counterfactual explanations have been a popular method of post-hoc explainability for a variety of settings in Machine Learning. Such methods focus on explaining classifiers by generating new data points that are similar to a given reference, while receiving a more desirable prediction. In this work, we investigate a framing for counterfactual generation methods that considers counterfactuals not as independent draws from a region around the reference, but as jointly sampled with the reference from the underlying data distribution. Through this framing, we derive a distance metric, tailored for counterfactual similarity that can be applied to a broad range of settings. Through both quantitative and qualitative analyses of counterfactual generation methods, we show that this framing allows us to express more nuanced dependencies among the covariates.

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Joshua Nathaniel Williams, Anurag Katakkar, Hoda Heidari, J. Zico Kolter. 2024-10-18. Rethinking Distance Metrics for Counterfactual Explainability. https://arxiv.org/abs/2410.14522

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