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作者：Ghassami, Amiremad; Yang, Alan; Shpitser, Ilya; Tchetgen, Eric Tchetgen
作者单位：Boston University; Stanford University; Johns Hopkins University; University of Pennsylvania
摘要：Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators that are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends cla...
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作者：Han, Ruijian; Luo, Lan; Lin, Yuanyuan; Huang, Jian
作者单位：Hong Kong Polytechnic University; Rutgers University System; Chinese University of Hong Kong
摘要：We propose a debiased stochastic gradient descent algorithm for online statistical inference with high-dimensional data. Our approach combines the debiasing technique developed in high-dimensional statistics with the stochastic gradient descent algorithm. It can be used to construct confidence intervals efficiently in an online fashion. Our proposed algorithm has several appealing aspects: as a one-pass algorithm, it reduces the time complexity; in addition, each update step requires only the ...
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作者：Samyak, Rajanala; Palacios, Julia A.
作者单位：Stanford University
摘要：Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees h...
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作者：Calissano, Anna; Feragen, Aasa; Vantini, Simone
作者单位：Polytechnic University of Milan; Technical University of Denmark
摘要：Statistical analysis for populations of networks is widely applicable, but challenging, as networks have strongly non-Euclidean behaviour. Graph space is an exhaustive framework for studying populations of unlabelled networks that are weighted or unweighted, uni- or multilayered, directed or undirected. Viewing graph space as the quotient of a Euclidean space with respect to a finite group action, we show that it is not a manifold, and that its curvature is unbounded from above. Within this ge...
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作者：Auerbach, Eric; Auerbach, Jonathan; Tabord-Meehan, Max
作者单位：Northwestern University; George Mason University; University of Chicago
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作者：Li, Canhui; Zeng, Donglin; Zhu, Wensheng
作者单位：Northeast Normal University - China; Northeast Normal University - China; University of Michigan System; University of Michigan; Yunnan University
摘要：One of the most important problems in precision medicine is to find the optimal individualized treatment rule, which is designed to recommend treatment decisions and maximize overall clinical benefit to patients based on their individual characteristics. Typically, the expected clinical outcome is required to be estimated first, for which an outcome regression model or a propensity score model usually needs to be assumed with most existing statistical methods. However, if either model assumpti...
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作者：Kono, Haruki
作者单位：Massachusetts Institute of Technology (MIT)
摘要：We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit...
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作者：Oliviero-Durmus, Alain; Moulines, Eric
作者单位：Institut Polytechnique de Paris; ENSTA Paris; Ecole Polytechnique
摘要：While the Metropolis-adjusted Langevin algorithm is a popular and widely used Markov chain Monte Carlo method, very few papers derive conditions that ensure its convergence. In particular, to the authors' knowledge, assumptions that are both easy to verify and guarantee geometric convergence, are still missing. In this work, we establish V-uniformly geometric convergence for the Metropolis-adjusted Langevin algorithm under mild assumptions about the target distribution. Unlike previous work, w...
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作者：Tan, Zhiqiang
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：We consider sensitivity analysis for causal inference in a longitudinal study with time-varying treatments and covariates. It is of interest to assess the worst-case possible values of counterfactual outcome means and average treatment effects under sequential unmeasured confounding. We formulate several multi-period sensitivity models to relax the corresponding versions of the assumption of sequential non-confounding. The primary sensitivity model involves only counterfactual outcomes, wherea...
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作者：Maity, Subha; Dutta, Diptavo; Terhorst, Jonathan; Sun, Yuekai; Banerjee, Moulinath
作者单位：University of Michigan System; University of Michigan; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
摘要：We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approac...
