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作者：Dukes, O.; Richardson, D. B.; Shahn, Z.; Robins, J. M.; Tchetgen, E. J. Tchetgen
作者单位：Ghent University; University of California System; University of California Irvine; City University of New York (CUNY) System; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
摘要：Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. In this paper, we show how one can potentially identify causal effects under violations of these assumptions by harnessing a negative control population or outcome. This strategy allows one to leverage subpopulations for whom the exposure is degenerate, and requires that the instrument-outcome association satisfies a ...
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作者：Luo, Jiyu; Rava, Denise; Bradic, Jelena; Xu, Ronghui
作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego
摘要：In this article we consider the marginal structural Cox model, which has been widely used to analyse observational studies with survival outcomes. The standard inverse probability weighting method under the model hinges on a propensity score model for the treatment assignment and a censoring model that incorporates both the treatment and the covariates. In such settings model misspecification can often occur, and the Cox regression model's non-collapsibility has historically posed challenges w...
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作者：Cattaneo, Matias D.; Han, Fang; Lin, Zhexiao
作者单位：Princeton University; University of Washington; University of Washington Seattle; University of California System; University of California Berkeley
摘要：In two influential contributions, Rosenbaum (2005, 2020a) advocated for using the distances between componentwise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of average treatment effects. While the intuitive benefits of using covariate ranks for matching estimation are apparent, there is no theoretical understanding of such procedures in the literature. We fill this gap by demonstrating that Rosenbaum's rank-based matching e...
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作者：Sang, Peijun; Kong, Dehan; Yang, Shu
作者单位：University of Waterloo; University of Toronto; North Carolina State University
摘要：Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serve as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis, often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven by outcome-related processe...
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作者：Henzi, Alexander; Shen, Xinwei; Law, Michael; Buhlmann, Peter
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：In recent years, there has been growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data. While most of the related research focuses on point predictions with the squared error loss, this article turns the focus towards probabilistic predictions, which aim to comprehensively quantify the uncertainty of an outcome variable given covariates. Within a causality-inspired framework, we investigate the invariance and robustness...
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作者：Cheng, Chao; Li, Fan
作者单位：Yale University
摘要：The causal inference literature frequently focuses on estimating the mean of the potential outcome, whereas quantiles of the potential outcome may carry important additional information. We propose an inverse estimating equation framework to generalize a wide class of causal inference solutions from estimating the mean of the potential outcome to its quantiles. We assume that a moment function is available to identify the mean of the threshold-transformed potential outcome, based on which a co...
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作者：Zhang, Haobo; Lu, Weihao; Lin, Qian
作者单位：Tsinghua University
摘要：The generalization ability of kernel interpolation in large dimensions, ie, $ n\asymp d<^>{\gamma} $ for some $ \gamma \gt 0 $, could be one of the most interesting problems in the recent renaissance of kernel regression, since it may help us understand the so-called benign overfitting phenomenon reported in the neural networks literature. Focusing on the inner product kernel on the unit sphere, we fully characterize the exact order of both the variance and the bias of large-dimensional kernel...
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作者：Saefken, B.; Kneib, T.; Wood, S. N.
作者单位：TU Clausthal; University of Gottingen; University of Edinburgh
摘要：The smoothing parameters in a semiparametric model are estimated based on criteria such as generalized cross-validation or restricted maximum likelihood. As these parameters are estimated in a data-driven manner, they influence the degrees of freedom of a semiparametric model, based on Stein's lemma. This allows us to associate parts of the degrees of freedom of a semiparametric model with the smoothing parameters. A framework is introduced that enables these degrees of freedom of the smoothin...
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作者：Zu, Tianhai; Qin, Yichen
作者单位：University of Texas System; University of Texas at San Antonio; University System of Ohio; University of Cincinnati
摘要：In network analysis, one frequently needs to conduct inference for network parameters based on a single observed network. Since the sampling distribution of the statistic is often unknown, one has to rely on the bootstrap. However, because of the complex dependence structure among vertices, existing bootstrap methods often yield unsatisfactory performance, especially for small or moderate sample sizes. Here we propose a new network bootstrap procedure, termed the local bootstrap, for estimatin...
