2026

最新文章

• Proximal causal inference for conditional separable effects AI翻译
  作者：Park, Chan; Stensrud, Mats J.; Tchetgen Tchetgen, Eric J.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Pennsylvania
  摘要：Scientists regularly pose questions about treatment effects on outcomes conditional on a posttreatment event. However, causal inference in such settings requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand that corresponds to scientifically meaningful questions in these settings. However, existing results for the CSE require no unmeasured confounding between the outcome and posttreatment e...
• A stratified L2-discrepancy with application to space-filling designs AI翻译
  作者：Tian, Ye; Xu, Hongquan
  作者单位：Beijing University of Posts & Telecommunications; University of California System; University of California Los Angeles
  摘要：Space-filling designs are widely used in computer experiments. We propose a stratified L2-discrepancy to evaluate the uniformity of a design when the design domain is stratified into various subregions. Weights are used to adjust preferences for the uniformity over subregions in each stratification. The stratified L2-discrepancy is easy to compute, satisfies a Koksma-Hlawka type inequality, and overcomes the curse of dimensionality that exists for other discrepancies. It is applicable to a bro...
• Optimal clustering by Lloyd's algorithm for low-rank mixture model AI翻译
  作者：Lyu, Zhongyuan; Xia, Dong
  作者单位：Hong Kong University of Science & Technology
  摘要：This article investigates the computational and statistical limits in clustering matrix-valued observations. We propose a low-rank mixture model (LrMM), adapted from the classical Gaussian mixture model (GMM), to handle matrix-valued observations, assuming low-rankness for population centre matrices. A computationally efficient clustering method is designed by integrating Lloyd's algorithm and low-rank approximation. Once well-initialized, the algorithm converges fast and achieves an exponenti...
• Selective randomization inference for adaptive experiments AI翻译
  作者：Freidling, Tobias; Zhao, Qingyuan; Gao, Zijun
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Cambridge; University of Southern California
  摘要：Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Because the null hypothesis and experimental design are data-dependent, it has long been recognized that statistical inference for adaptive experiments is not straightforward. Most existing methods only apply to specific adaptive designs and rely on strong assumptions. In this work, we propose selective randomization infer...
• Melanie Weber's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Weber, Melanie
  作者单位：Harvard University
• Augmented balancing weights as linear regression AI翻译
  作者：Bruns-Smith, David; Dukes, Oliver; Feller, Avi; Ogburn, Elizabeth L.
  作者单位：Stanford University; Ghent University; University of California System; University of California Berkeley; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• Liyang Sun's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Sun, Liyang
  作者单位：University of London; University College London
• Supriya Tiwari and Pallavi Basu's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Tiwari, Supriya; Basu, Pallavi
  作者单位：Indian School of Business (ISB)
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• On the statistical analysis of grouped data: when Pearson χ2 and other divisible statistics are not goodness-of-fit AI翻译
  作者：Algeri, Sara; Khmaladze, Estate, V
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Victoria University Wellington
  摘要：Thousands of experiments are analysed, and papers are published each year involving the statistical analysis of grouped data. While this area of statistics is often perceived-somewhat naively-as saturated, several misconceptions still affect everyday practice, and new frontiers have so far remained unexplored. Researchers must be aware of the limitations affecting their analyses and what new possibilities are at their hands. The article introduces a unifying approach to the analysis of divisib...
• Quantifying individual risk for binary outcomes AI翻译
  作者：Wu, Peng; Ding, Peng; Geng, Zhi; Liu, Yue
  作者单位：Beijing Technology & Business University; University of California System; University of California Berkeley; Renmin University of China; Renmin University of China
  摘要：Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widely used to capture treatment effect heterogeneity induced by observed covariates and to design individualized treatment policies. However, it is an average metric within subpopulations, which prevents it from revealing individual risk, potentially leading to misleading results. This article fills this gap by examining...