Counterfactual Explanations for Deep Two-Sample Testing
Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such as images. Recent deep two-sample tests improve sensitivity in these settings by learning informative representations, yet they provide limited insight into which data features drive rejection of the null hypothesis $H_0$. To address this issue, we propose a counterfactual explanation framework for deep two-sample testing that generates sample-level edits moving observations from a source group toward a target group while explicitly reducing the discrepancy measured by the...
| Authors | Wei-Cheng Lai, Marco Simnacher, Christoph Lippert |
|---|---|
| Primary category | stat.ML |
| Published | 29 May 2026 |
| Last updated at source | 11 June 2026 |
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