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作者：Goplerud, Max; Imai, Kosuke; Pashley, Nicole E.
作者单位：University of Texas System; University of Texas Austin; Harvard University; Harvard University; Rutgers University System; Rutgers University New Brunswick
摘要：Estimation of heterogeneous treatment effects is an active area of research. Most of the existing methods, however, focus on estimating the conditional average treatment effects of a single, binary treatment given a set of pretreatment covariates. In this paper we propose a method to estimate the heterogeneous causal effects of high-dimensional treatments, which poses unique challenges in terms of estimation and interpretation. The proposed approach finds maximally heterogeneous groups and use...
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作者：Boxer, Kate S.; Hong, Boyeong; Kontokosta, Constantine E.; Neill, Daniel B.
作者单位：New York University; New York University Tandon School of Engineering; New York University
摘要：Systems such as 311 enable residents of a community to report on their environments and to request nonemergency municipal services. While such systems provide an important link between community and government, resident-generated data suffer from reporting bias, with some subpopulations reporting at lower rates than others. Our research focuses on defining the underreporting of heating and hot water problems to New York City's 311 system and developing methods to estimate under-reporting. Firs...
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作者：Wang, Tian; Li, Bing; Xu, Huang; Miao, Yuqi; Qian, Min; Wang, Shuang
作者单位：Columbia University; Brown University
摘要：Many statistical methods examining associations and predictions of microbiome on health outcomes are distance-based, where several distance metrics are calculated to capture different aspects of microbiome and the optimal one is selected for final association or prediction. Studies have suggested that diverse forms of taxa are linked to health outcomes; that is, both abundant taxa in close proximity and rare taxa far away on the phylogenetic tree could be associated with or predictive of the s...
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作者：Angelopoulos, Anastasios N.; Bates, Stephen; Candes, Emmanuel J.; Jordan, Michael, I; Lei, Lihua
作者单位：University of California System; University of California Berkeley; Massachusetts Institute of Technology (MIT); Stanford University; Stanford University
摘要：We introduce a framework for calibrating machine learning models to satisfy finite-sample statistical guarantees. Our calibration algorithms work with any model and (unknown) data-generating distribution and do not require retraining. The algorithms address, among other examples, false discovery rate control in multilabel classification, intersection-over-union control in instance segmentation, and simultaneous control of the type-1 outlier error and confidence set coverage in classification o...
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作者：Zhang, Dongxue; Feng, Long; Wu, Yujia; Lan, Wei; Zhou, Jing
作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Nankai University; Nankai University; Renmin University of China; Renmin University of China
摘要：Ever since its outbreak, COVID-19 has been rapidly spreading around the world and has become a significant threat to public health. Past experience has shown that, because of the incubation period, the contemporaneous population flow does not affect the contemporaneous number of cases, but the time-lagged population flow can affect case numbers. Moreover, the population flow networks of different lags can exhibit varying influences on the transmission of COVID-19. However, most existing studie...
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作者：Liu, Xueqing; Deliu, Nina; Chakraborty, Tanujit; Bell, Lauren; Chakraborty, Bibhas
作者单位：National University of Singapore; Sapienza University Rome; Sorbonne University Abu Dhabi; University of London; King's College London
摘要：Mobile health (mHealth) interventions often aim to improve distal outcomes, such as clinical conditions, by optimizing proximal outcomes through just-in-time adaptive interventions. Contextual bandits provide a suitable framework for customizing such interventions according to individual time-varying contexts. However, unique challenges, such as modeling count outcomes within bandit frameworks, have hindered the widespread application of contextual bandits to mHealth studies. The current work ...
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作者：Liu, Xin; Schnell, Patrick M.
作者单位：University System of Ohio; Ohio State University
摘要：Electronic medical records (EMR) data contain rich information that can facilitate health-related studies but is collected primarily for purposes other than research. For recurrent events, EMR data often do not record event times or counts but only contain intermittently assessed and censored observations (i.e., upper and/or lower bounds for counts in a time interval) at uncontrolled times. This can result in noncontiguous or overlapping assessment intervals with censored event counts. Existin...
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作者：Mondal, Debashis; Chang, Xiaohui
作者单位：Washington University (WUSTL); Oregon State University
摘要：Environmental bioassays, such as sediment toxicity tests, provide abroad survey of toxicity that is crucial for the conservation and protection of marine and estuarine ecosystems. Using odds, risk, and survival probability ratios, this paper presents a critical evaluation of sediment toxicity tests data collected in the New York-New Jersey harbor area. It further derives spatial regression analysis to combine test results, predict toxicity at unsampled locations, and determine the effects of s...
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作者：Medom-Nnamdi, Patrick; Smith, Timothy R.; Onnela, Jukka-Pekka; Lu, Junwei
作者单位：Harvard University; Harvard University; Harvard Medical School; Harvard University Medical Affiliates; Brigham & Women's Hospital
摘要：We propose a nonparametric additive model for estimating interpretable value functions in reinforcement learning, with an application in optimizing postoperative recovery through personalized, adaptive recommendations. While reinforcement learning has achieved significant success in various domains, recent methods often rely on black-box approaches, such as neural networks, which hinder the examination of individual feature contributions to a decision-making policy. Our novel method offers a f...
