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作者:Caughey, Devin; Dafoe, Allan; Li, Xinran; Miratrix, Luke
作者单位:Massachusetts Institute of Technology (MIT); University of Chicago; Harvard University; University of Chicago
摘要:Randomisation inference (RI) is typically interpreted as testing Fisher's 'sharp' null hypothesis that all unit-level effects are exactly zero. This hypothesis is often criticised as restrictive and implausible, making its rejection scientifically uninteresting. We show, however, that many randomisation tests are also valid for a 'bounded' null hypothesis under which the unit-level effects are all non-positive (or all non-negative) but are otherwise heterogeneous. In addition to being more pla...
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作者:Tian, Maozai; Yu, Keming; Wang, Jiangfeng
作者单位:Renmin University of China; Zhejiang Gongshang University; Brunel University
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作者:Zhang, Bo
作者单位:Chinese Academy of Sciences; University of Science & Technology of China, CAS
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作者:Fong, Edwin; Holmes, Chris; Walker, Stephen G.
作者单位:Alan Turing Institute; University of Oxford; University of Texas System; University of Texas Austin; University of Oxford
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作者:Fong, Edwin; Holmes, Chris; Walker, Stephen G.
作者单位:Alan Turing Institute; University of Oxford; University of Texas System; University of Texas Austin
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作者:Ray, Kolyan; Szabo, Botond
作者单位:Imperial College London; Bocconi University
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作者:Li, Shuangning; Liu, Molei
作者单位:Harvard University
摘要:The model-X conditional randomisation test (CRT) is a flexible and powerful testing procedure for testing the hypothesis X?Y divide Z. However, it requires perfect knowledge of X divide Z and may lose its validity when there is an error in modelling X divide Z. This problem is even more severe when Z is of high dimensionality. In response to this, we propose the Maxway CRT, which learns the distribution of Y divide Z and uses it to calibrate the resampling distribution of X to gain robustness ...
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作者:Qiu, Hongxiang; Dobriban, Edgar; Tchetgen, Eric Tchetgen
作者单位:University of Pennsylvania
摘要:Predicting sets of outcomes-instead of unique outcomes-is a promising solution to uncertainty quantification in statistical learning. Despite a rich literature on constructing prediction sets with statistical guarantees, adapting to unknown covariate shift-a prevalent issue in practice-poses a serious unsolved challenge. In this article, we show that prediction sets with finite-sample coverage guarantee are uninformative and propose a novel flexible distribution-free method, PredSet-1Step, to ...
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作者:Moran, Gemma E.; Blei, David M.; Ranganath, Rajesh
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作者:Pawel, Samuel; Held, Leonhard
作者单位:University of Zurich; Swiss School of Public Health (SSPH+)