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作者：Su, Yongchang; Li, Xinran
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Evaluating the treatment effect has become an important topic for many applications. However, most existing literature focuses mainly on average treatment effects. When the individual effects are heavy tailed or have outlier values, not only may the average effect not be appropriate for summarizing treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on quantiles of individual treatme...
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作者：Branson, Zach; Li, Xinran; Ding, Peng
作者单位：Carnegie Mellon University; University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Berkeley
摘要：Power analyses are an important aspect of experimental design, because they help determine how experiments are implemented in practice. It is common to specify a desired level of power and compute the sample size necessary to obtain that power. Such calculations are well known for completely randomized experiments, but there can be many benefits to using other experimental designs. For example, it has recently been established that rerandomization, where subjects are randomized until covariate...
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作者：Savje, F.
作者单位：Yale University
摘要：Exposure mappings facilitate investigations of complex causal effects when units interact in experiments. Current methods require experimenters to use the same exposure mappings to define the effect of interest and to impose assumptions on the interference structure. However, the two roles rarely coincide in practice, and experimenters are forced to make the often questionable assumption that their exposures are correctly specified. This paper argues that the two roles exposure mappings curren...
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作者：Yang, Cheng-Han; Cheng, Yu-Jen
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作者：Li, Guanxun; Zhang, Xianyang
作者单位：Beijing Normal University; Beijing Normal University Zhuhai; Texas A&M University System; Texas A&M University College Station
摘要：We discover a connection between the Benjamini-Hochberg procedure and the e-Benjamini-Hochberg procedure (Wang & Ramdas, 2022) with a suitably defined set of e-values. This insight extends to Storey's procedure and generalized versions of the Benjamini-Hochberg procedure and the model-free multiple testing procedure of Barber & Cand & eacute;s (2015) with a general form of rejection rules. We further summarize these findings in a unified form. These connections open up new possibilities for de...
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作者：Rytgaard, H. C. W.; van der Laan, M. J.
作者单位：University of Copenhagen; University of California System; University of California Berkeley
摘要：This paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two ...
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作者：Hanley, J. A.
作者单位：McGill University
摘要：Statisticians and epidemiologists generally cite the publications of and as the first description and use of conditional logistic regression, while economists cite the book chapter by Nobel laureate McFadden (). We describe the until-now-unrecognized use of, and way of fitting, this model in 1934 by Lionel Penrose and Ronald Fisher.
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作者：Leung, Michael P.
作者单位：University of California System; University of California Santa Cruz
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作者：Sadeghi, Kayvan; Soo, Terry
作者单位：University of London; University College London
摘要：Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions. Our axiomatizations neatly lead to a simple and clear theory of causality that has several advantages: it does not need to make use of any modelling assumptions such as those imposed by structural causal models; it r...
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作者：Wen, Mengtao; Jia, Yinxu; Ren, Haojie; Wang, Zhaojun; Zou, Changliang
作者单位：Nankai University; Shanghai Jiao Tong University
摘要：This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation, particularly the uniformity problem inherent in functional processes. To tackle this issue, we introduce a versatile framework designed to extract valuable information from unlabelled data by approximating a conditional distribution on covariates. T...
