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作者：Allen, Sam; Koh, Jonathan; Segers, Johan; Ziegel, Johanna
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Bern; KU Leuven; Universite Catholique Louvain
摘要：Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we intr...
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作者：Liu, Zhaoyang; Han, Tingxuan; Rubin, Donald B.; Deng, Ke
作者单位：Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua University
摘要：Rerandomization is a powerful tool for experiment-based causal inference because it can better balance covariates than classic randomized designs, thereby leading to more accurate causal effect estimation. However, basic rerandomization and some of its extensions do not prioritize covariates that believed to be strongly associated with potential outcomes. To address this limitation, and thereby create more efficient rerandomization procedures, the quantification of covariate heterogeneity is a...
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作者：Gaffi, Francesco; Durante, Daniele; Lijoi, Antonio; Prunster, Igor
作者单位：Bocconi University; University System of Maryland; University of Maryland College Park
摘要：Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are observed among the same set of nodes (edge-colored networks) or where a single notion of connectivity is measured between nodes belonging to different pre-specified layers (node-colored networks). While progress has been made in statistical modeling of edge-colored networks, principled approaches that flexibly account for...
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作者：Ben-Michael, Eli; Greiner, D. James; Imai, Kosuke; Jiang, Zhichao
作者单位：Carnegie Mellon University; Carnegie Mellon University; Harvard University; Harvard University; Harvard University; Sun Yat Sen University
摘要：Algorithmic recommendations and decisions have become ubiquitous in today's society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal is to analyze data f...
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作者：Fan, Xinyan; Fang, Kuangnan; Lan, Wei; Tsai, Chih-Ling
作者单位：Renmin University of China; Renmin University of China; Xiamen University; Xiamen University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of California System; University of California Davis
摘要：We propose a novel network-varying coefficient model that extends traditional varying coefficient models to accommodate network data. The main idea is to model the regression coefficients as the functions of the latent locations of network nodes that drive formation of the network. To estimate the model, we identify the latent locations via the latent space model and then develop an iterative projected gradient descent algorithm by optimizing the network parameters and regression coefficients ...
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作者：Wu, Peng; Luo, Shanshan; Geng, Zhi
作者单位：Beijing Technology & Business University
摘要：There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive comparative analyses. In this article, we focus on a typical scenario involving a source dataset and a target dataset. We first design six settings under covariate shift and conduct a comparative analysis by deriving the semiparametric efficiency bounds for the ATE in the target population. We then extend this analys...
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作者：Gruber, Luis; Kastner, Gregor; Bhattacharya, Anirban; Pati, Debdeep; Pillai, Natesh; Dunson, David
作者单位：University of Klagenfurt; Texas A&M University System; Texas A&M University College Station; University of Wisconsin System; University of Wisconsin Madison; Harvard University; Duke University
摘要：Bhattacharya et al. introduce a novel prior, the Dirichlet-Laplace (DL) prior, and propose a Markov chain Monte Carlo (MCMC) method to simulate posterior draws under this prior in a conditionally Gaussian setting. The original algorithm samples from conditional distributions in the wrong order, that is, it does not correctly sample from the joint posterior distribution of all latent variables. This note details the issue and provides two simple solutions: A correction to the original algorithm...
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作者：Athreya, Avanti; Lubberts, Zachary; Park, Youngser; Priebe, Carey
作者单位：Johns Hopkins University; University of Virginia; Johns Hopkins University
摘要：Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the evolution of latent vectors exhibits low-dimensional manifold structure under a suitable distance. This distance can be approximated by a measure of separation between the observed networks th...
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作者：Chan, Kwun Chuen Gary; Prentice, Ross L.; Yuan, Zhenman
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
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作者：He, Xianwen; Li, Yao
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：With the widespread application of machine learning algorithms in daily life, it is crucial to mitigate the risk of these algorithms producing socially undesirable outcomes that may disproportionately disadvantage certain groups or individuals based on demographic characteristics such as gender, race, or disabilities. In recent years, machine learning fairness has gained increasing attention from both researchers and the public. This article provides a comprehensive overview of fairness-enhanc...
