arXiv Machine Learning By Aaron Wei, Milad Jalali, Danica J. Sutherland

Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics

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arXiv:2512. 13997v2 Announce Type: replace-cross Abstract: Existing two-sample testing techniques, particularly those based on choosing a kernel for the Maximum Mean Discrepancy (MMD), often assume equal sample sizes from the two distributions.

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arXiv Machine Learning
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Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

The paper introduces a new estimand for conditional distributional treatment effects that captures how treatments influence the entire outcome distribution, including variance and tail risks, in a covariate-dependent manner. It presents a doubly robust estimator that is minimax optimal locally and uses it to construct a test for global homogeneity of conditional potential outcome distributions. The test accommodates discrepancies beyond the maximum mean discrepancy, guarantees valid type‑1 error, is consistent against fixed alternatives, and includes a computationally efficient, permutation‑free algorithm with exact closed‑form expressions for two natural discrepancies.

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