Rethinking Distance Metrics for Counterfactual Explainability

• URL: http://arxiv.org/abs/2410.14522v1
• Date: Fri, 18 Oct 2024 15:06:50 GMT
• Title: Rethinking Distance Metrics for Counterfactual Explainability
• Authors: Joshua Nathaniel Williams, Anurag Katakkar, Hoda Heidari, J. Zico Kolter,
• Abstract summary: 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. We derive a distance metric, tailored for counterfactual similarity that can be applied to a broad range of settings.
• Score: 53.436414009687
• License:
• 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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