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作者：Wang, Jiping; Li, Rong; Chang, Wei-Shan; Hsiao, Kai-Yuan; Shia, Ben-Chang; Ma, Shuangge
作者单位：Yale University; Fu Jen Catholic University; Fu Jen Catholic University
摘要：The analysis of clinical treatment measures has been extensively conducted and can facilitate more effective resource management and assist in better understanding diseases. Most of the existing analyses have been focused on a single disease or many diseases combined. Partly motivated by the successes of gene-centric and phenotypic human disease network (HDN) research, there has been growing interest in network analysis of clinical treatment measures. However, existing studies have been limite...
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作者：Bargagli-Stoffi, Falco J.; Tortu, Costanza; Forastiere, Laura
作者单位：University of California System; University of California Los Angeles; Scuola Superiore Sant'Anna; Yale University
摘要：The bulk of causal inference studies rule out the presence of interference between units. However, in many real-world scenarios, units are interconnected by social, physical, or virtual ties, and the effect of the treatment can spill from one unit to other connected individuals in the network. In this paper, we develop a machine learning method that uses tree-based algorithms and a Horvitz-Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individ...
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作者：Huang, Melody
作者单位：Yale University
摘要：Estimating externally valid causal effects is a foundational problem in the social and biomedical sciences. Generalizing or transporting causal estimates from an experimental sample to a target population of interest relies on an overlap (or positivity) assumption between the experimental sample and the target population. In practice, having full overlap between an experimental sample and a target population can be implausible. In the following paper, we introduce a framework for considering e...
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作者：Ibrahim, Shibal; Radchenko, Peter; Ben-David, Emanuel; Mazumder, Rahul
作者单位：Massachusetts Institute of Technology (MIT); University of Sydney; Massachusetts Institute of Technology (MIT)
摘要：In this paper we consider the problem of predicting survey response rates using a family of flexible and interpretable nonparametric models. The study is motivated by the U.S. Census Bureau's well-known ROAM application, which uses a linear regression model trained on the U.S. Census Planning Database data to identify hard-to-survey areas. A crowdsourcing competition (Public Opin. Q. 81 (2016) 144-156) organized more than 10 years ago revealed that machine learning methods, based on ensembles ...
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作者：Yu, Ang; Elwert, Felix
作者单位：University of Wisconsin System; University of Wisconsin Madison
摘要：We introduce a new nonparametric causal decomposition approach that identifies the mechanisms by which a treatment variable contributes to a group-based outcome disparity. Our approach distinguishes three mechanisms: group differences in: (1) treatment prevalence, (2) average treatment effects, and (3) selection into treatment based on individual-level treatment effects. Our approach reformulates classic Kitagawa-Blinder-Oaxaca decompositions in causal and nonparametric terms, complements caus...
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作者：Luo, Fangzhi; Tan, Jianbin; Zhang, Donglan; Huang, Hui; Shen, Ye
作者单位：University System of Georgia; University of Georgia; Duke University; Renmin University of China; Renmin University of China
摘要：Understanding the longitudinally changing associations between Social Determinants of Health (SDOH) and stroke mortality is essential for effective stroke management. Previous studies have uncovered significant regional disparities in the associations between SDOH and stroke mortality. However, existing studies have not utilized longitudinal associations to develop data-driven methods for regional division in stroke control. To fill this gap, we propose a novel clustering method to analyze SDO...
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作者：Maier, Eva-Maria; Stoecker, Almond; Fitzenberger, Bernd; Greven, Sonja
作者单位：Humboldt University of Berlin; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Erlangen Nuremberg; University of Erlangen Nuremberg
摘要：Motivated by research on gender identity norms and the distribution of the woman's share in a couple's total labor income, we consider additive regression models for densities as responses with scalar covariates. To preserve nonnegativity and integration to one under vector space operations, we formulate the model for densities in a Bayes Hilbert space, which allows to not only consider continuous densities but also, for example, discrete or mixed densities. Mixed ones occur in our application...
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作者：Yan, Lei; Zhang, Xin; Lan, Zhou; Bandyopadhyay, Dipankar; Wu, Yichao
作者单位：State University System of Florida; Florida State University; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard University; Harvard Medical School; Virginia Commonwealth University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Modern applications in medical imaging often include high-dimensional predictors and spatially dependent responses in the non-Euclidean space. For example, in imaging-genetics studies, our objective is to study the relationship between single-nucleotide polymorphisms (SNPs), a high-dimensional predictor vector, and diffusion tensor imaging (DTI) responses, which are thousands to millions of voxelwise 3 x 3 symmetric positive definite (SPD) matrices. In this paper we develop a fast and pragmati...
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作者：Douwes-Schultz, Dirk; Schmidt, Alexandra m.; Shen, Yannan; Buckeridge, David l.
作者单位：McGill University
摘要：Recurrent COVID-19 outbreaks have placed immense strain on the hospital system in Quebec. We develop a Bayesian three-state coupled Markov admissions in the 30 largest hospitals. Within each catchment area, we assume the existence of three states for the disease: absence, a new state meant to account for many zeroes in some of the smaller areas; endemic and outbreak. Then we assume the disease switches between the three states in each area through a series of coupled nonhomogeneous hidden Mark...
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作者：Wang, Ye; Samii, Cyrus; Chang, Haoge; Aronow, P. M.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; New York University; Columbia University; Yale University
摘要：We consider design-based causal inference for spatial experiments in which treatments may have effects that bleed out and feed back in complex ways. Such spatial spillover effects violate the no interference assumption for standard causal inference methods. The complexity of spatial spillover effects also raises the risk of misspecification and bias in model-based analyses. We offer an approach for robust inference in such settings without having to specify a parametric outcome model. We defin...
