Counterfactual Explanations for Deep Two-Sample Testing

📰 ArXiv cs.AI

Learn to generate counterfactual explanations for deep two-sample testing to understand which data features drive hypothesis rejection

advanced Published 4 Jun 2026

Action Steps

Apply deep two-sample testing to identify distributional differences in high-dimensional data
Generate counterfactual explanations using techniques such as generative models or perturbation methods
Analyze the counterfactual explanations to identify key features driving hypothesis rejection
Use the insights gained to refine the model or collect more targeted data
Evaluate the effectiveness of the counterfactual explanations in improving model interpretability

Who Needs to Know This

Data scientists and ML engineers working with high-dimensional data can benefit from this technique to improve model interpretability and identify key features driving distributional differences

Key Insight

💡 Counterfactual explanations can provide insights into which data features drive rejection of the null hypothesis in deep two-sample testing

Key Takeaways

Learn to generate counterfactual explanations for deep two-sample testing to understand which data features drive hypothesis rejection

Full Article

Title: Counterfactual Explanations for Deep Two-Sample Testing

Abstract:
arXiv:2606.04009v1 Announce Type: cross Abstract: 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 addr

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