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.
@article{arxiv.2410.14522,
title = {Rethinking Distance Metrics for Counterfactual Explainability},
author = {Joshua Nathaniel Williams and Anurag Katakkar and Hoda Heidari and J. Zico Kolter},
journal= {arXiv preprint arXiv:2410.14522},
year = {2024}
}