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作者：Liu, Qiao; Wong, Wing Hung
作者单位：Yale University; Yale University; Stanford University
摘要：Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and out...
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作者：Tang, Yin; Li, Bing
作者单位：University of Kentucky; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We introduce a unified, flexible, and easy-to-implement framework of sufficient dimension reduction that can accommodate both linear and nonlinear dimension reduction, and both the conditional distribution and the conditional mean as the targets of estimation. This unified framework is achieved by a specially structured neural network-the Belted and Ensembled Neural Network (BENN)-that consists of a narrow latent layer, which we call the belt, and a family of transformations of the response, w...
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作者：Spagnolo, Francesco Schirripa; Salvati, Nicola; Bertarelli, Gaia; Haziza, David; Chambers, Ray
作者单位：University of Pisa; Universita Ca Foscari Venezia; University of Ottawa; Australian National University
摘要：Projective outlier-robust M-quantile-based small area estimators can be substantially biased when the sample data contain representative outliers. In this article we propose two new predictive type bias corrected versions of these estimators for continuous and discrete outcomes. Given both area level and individual level outliers in the population, these new estimators are more efficient than the robust-predictive and robust-projective estimators that have been proposed in the small area estim...
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作者：Liu, Qiao; Wong, Wing Hung
作者单位：Yale University; Yale University; Stanford University
摘要：Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and out...
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作者：Tang, Yin; Li, Bing
作者单位：University of Kentucky; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：We introduce a unified, flexible, and easy-to-implement framework of sufficient dimension reduction that can accommodate both linear and nonlinear dimension reduction, and both the conditional distribution and the conditional mean as the targets of estimation. This unified framework is achieved by a specially structured neural network-the Belted and Ensembled Neural Network (BENN)-that consists of a narrow latent layer, which we call the belt, and a family of transformations of the response, w...
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作者：Spagnolo, Francesco Schirripa; Salvati, Nicola; Bertarelli, Gaia; Haziza, David; Chambers, Ray
作者单位：University of Pisa; Universita Ca Foscari Venezia; University of Ottawa; Australian National University
摘要：Projective outlier-robust M-quantile-based small area estimators can be substantially biased when the sample data contain representative outliers. In this article we propose two new predictive type bias corrected versions of these estimators for continuous and discrete outcomes. Given both area level and individual level outliers in the population, these new estimators are more efficient than the robust-predictive and robust-projective estimators that have been proposed in the small area estim...
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作者：de Punder, Ramon F. A.; Diks, Cees G. H.; Laeven, Roger J. A.; van Dijk, Dick J. C.
作者单位：University of Amsterdam; Tinbergen Institute; Tilburg University; Eindhoven University of Technology; Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC
摘要：When comparing predictive distributions, forecasters are typically not equally interested in all regions of the outcome space. To address the demand for focused forecast evaluation, we propose a procedure to transform strictly proper scoring rules into their localized counterparts while preserving the score divergence and strict propriety. This is accomplished by applying the original scoring rule to a censored distribution. Our procedure nests the censored likelihood score as a special case. ...
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作者：Cai, Bryan; Cui, Ying; Fu, Haoda; Lloyd-Jones, Donald M.; Zhao, Lihui; Lu, Tian
作者单位：Stanford University; Stanford University; Eli Lilly; Northwestern University; Feinberg School of Medicine
摘要：Physicians today have access to a variety of tests for diagnosing and prognosticating medical conditions. Ideally, they would apply a high-quality prediction model using all relevant features to facilitate appropriate decision-making (e.g., treatment selection; risk assessment). However, some of these features incur additional costs and are not readily available to patients and physicians. In practice, predictors are typically gathered sequentially, that is, physicians continually evaluate inf...
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作者：Chen, Canyi; Qiao, Nan; Zhu, Liping
作者单位：University of Michigan System; University of Michigan; Renmin University of China; Renmin University of China
摘要：This article concerns efficiently classifying high-dimensional data over decentralized networks. Penalized support vector machines (SVMs) are widely used for high-dimensional classification tasks. However, the double nonsmoothness of the objective function poses significant challenges in developing efficient decentralized learning methods. Existing approaches frequently suffer from slow, sublinear convergence rates. To address this issue, we consider a convolution-based smoothing technique for...
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作者：Zhang, Linjun; Li, Lexin
作者单位：Rutgers University System; Rutgers University New Brunswick; University of California System; University of California Berkeley
