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作者：Wang, Yixin; Blei, David M.
作者单位：Columbia University; Columbia University
摘要：Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods assume that we observe all confounders, variables that affect both the causal variables and the outcome variables. This assumption is standard but it is also untestable. In this article, we develop the deconfounder, a way to do causal inference with weaker assumptions than the traditional methods require. The deconfounder is designed for problems of multiple causal inference: scienti...
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作者：Shao, Stephane; Jacob, Pierre E.; Ding, Jie; Tarokh, Vahid
作者单位：Harvard University; University of Minnesota System; University of Minnesota Twin Cities; Duke University
摘要：The Bayes factor is a widely used criterion in model comparison and its logarithm is a difference of out-of-sample predictive scores under the logarithmic scoring rule. However, when some of the candidate models involve vague priors on their parameters, the log-Bayes factor features an arbitrary additive constant that hinders its interpretation. As an alternative, we consider model comparison using the Hyvarinen score. We propose a method to consistently estimate this score for parametric mode...
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作者：Francom, Devin; Sanso, Bruno; Bulaevskaya, Vera; Lucas, Donald; Simpson, Matthew
作者单位：United States Department of Energy (DOE); Los Alamos National Laboratory; University of California System; University of California Santa Cruz; United States Department of Energy (DOE); Lawrence Livermore National Laboratory
摘要：An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of the release (location, amount, and duration) and meteorological conditions is an active research area highly relevant for emergency and long-term response to these releases. As a result, researchers have developed particle dispersion simulators to provide plume path predictions that incorporate releas...
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作者：D'Amour, Alexander
作者单位：Alphabet Inc.; Google Incorporated
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作者：Jackson, Christopher; Presanis, Anne; Conti, Stefano; De Angelis, Daniela
作者单位：MRC Biostatistics Unit; University of Cambridge
摘要：Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting da...
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作者：Zhang, Kai
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：We study the problem of nonparametric dependence detection. Many existing methods may suffer severe power loss due to nonuniform consistency, which we illustrate with a paradox. To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat) and binary expansion testing (BET), which examine dependence through a novel binary expansion filtration approximation of the copula. Through a Hadamard transform, we find that ...
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作者：Chakraborty, Shubhadeep; Zhang, Xianyang
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：Many statistical applications require the quantification of joint dependence among more than two random vectors. In this work, we generalize the notion of distance covariance to quantify joint dependence among random vectors. We introduce the high-order distance covariance to measure the so-called Lancaster interaction dependence. The joint distance covariance is then defined as a linear combination of pairwise distance covariances and their higher-order counterparts which together completely ...
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作者：Imai, Kosuke; Jiang, Zhichao
作者单位：Harvard University; Harvard University; University of Massachusetts System; University of Massachusetts Amherst
摘要：We begin by congratulating Yixin Wang and David Blei for their thought-provoking article that opens up a new research frontier in the field of causal inference. The authors directly tackle the challenging question of how to infer causal effects of many treatments in the presence of unmeasured confounding. We expect their article to have a major impact by further advancing our understanding of this important methodological problem. This commentary has two goals. We first critically review the d...
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作者：Li, Gang; Wang, Xiaoyan
作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
摘要：This article develops a pair of new prediction summary measures for a nonlinear prediction function with right-censored time-to-event data. The first measure, defined as the proportion of explained variance by a linearly corrected prediction function, quantifies the potential predictive power of the nonlinear prediction function. The second measure, defined as the proportion of explained prediction error by its corrected prediction function, gauges the closeness of the prediction function to i...
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作者：Knox, Dean; Yamamoto, Teppei; Baum, Matthew A.; Berinsky, Adam J.
作者单位：Princeton University; Massachusetts Institute of Technology (MIT); Harvard University
摘要：Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants' preferences over trea...
