Counterfactual Explanations for Deep Two-Sample Testing
Researchers developed a method to generate counterfactual explanations for deep two-sample tests, which detect distributional differences in high-dimensional data like images. The approach combines diffusion autoencoders with deep learning models to identify which data features drive statistical differences between groups. This work addresses a key limitation of existing tests by providing interpretable, sample-level insights into what features distinguish distributions.
Two-sample testing is a statistical method for detecting differences between distributions, but classical approaches struggle with high-dimensional structured data such as medical images. Recent deep learning-based tests improve sensitivity but lack interpretability—they identify that differences exist without explaining which features cause them. The authors propose a counterfactual explanation framework that generates minimal, plausible edits to samples, moving them from a source group toward a target group while reducing the test's measured discrepancy. The method optimizes a maximum mean discrepancy (MMD) objective in the learned representation space and is evaluated on synthetic 2D shapes and MRI cohorts. Results show that counterfactual edits consistently increase p-values, indicating the edited source set becomes statistically closer to the target distribution, with localized MRI changes aligning with known anatomical differences between cohorts.
What's missing
The paper does not discuss computational complexity or scalability to very large datasets, nor does it compare performance against alternative interpretability methods for two-sample tests (e.g., saliency maps, attention mechanisms, or other explanation frameworks).
What different sources said
- arXiv cs.AICenter
Counterfactual Explanations for Deep Two-Sample Testing
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