stat.ME · 2026-07-27 · No. 66

Methodology, 2026-07-27.

3 new papers in stat.ME. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Interventional Score Geometry for Causal Inference

    Mojtaba Eslami

    stat.ME · cs.AI · econ.EM

    Let $p(x)$ be the joint density of variables $X$, and let $ψ(x)=\nabla_x\log p(x)$ be its score field. Geometry constructed from $p$ and $ψ$ alone cannot identify causal direction: structural models with the same observational distribution have the same score geometry. I develop an interventional analogue. A hard intervention $\operatorname{do}(X_k=ξ)$ does not merely reweight the joint law; it restricts the distribution to the submanifold...

    arxiv.org/abs/2607.21914 · PDF

  2. 02

    Distributional Determinantal Point Process for Repulsive Clustering of Distributions

    Khai Nguyen, Yang Ni, Elizabeth Juarez-Colunga, Peter Mueller

    stat.ME · cs.LG · stat.AP · stat.CO · stat.ML

    We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather than points in a real space. The dDPP is constructed via an L-ensemble with a sliced Wasserstein (SW) kernel between distributions. We show its validity as a well-defined point process. In the discrete setting, we derive concentration results for plug-in estimators of the L-ensemble, the...

    arxiv.org/abs/2607.21847 · PDF

  3. 03

    Longitudinal Random Forests for Sparse and Irregular Response Trajectories

    Yangsheng Wang, Xiaotian Dai, Haoda Fu, Guifang Fu

    stat.ME · cs.LG · stat.ML

    Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points. Such heterogeneity is often driven by subject-specific covariates, yet existing methods have been restricted to a scalar endpoint value, completely neglecting the underlying response trajectories. We propose a novel Longitudinal Random Forest (LRF) framework that leverages tree-based ensemble machine learning with adaptive node-wise longitudinal...

    arxiv.org/abs/2607.21817 · PDF

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