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作者：Meng, Xiao-Li
作者单位：Harvard University
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作者：Xie, Zhongming; Zhang, Wanheng; Wang, Jingshen; Wu, Chong
作者单位：University of California System; University of California Berkeley; University of Texas System; UTMD Anderson Cancer Center
摘要：In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses using summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropy issues. To address ...
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作者：Luo, Tianpai; Wu, Weichi
作者单位：Tsinghua University
摘要：We propose a new framework for the simultaneous inference of monotone and smoothly time-varying functions under complex temporal dynamics. This will be done using the monotone rearrangement and the nonparametric estimation. We capitalize the Gaussian approximation for the nonparametric monotone estimator and construct the asymptotically correct simultaneous confidence bands (SCBs) using designed bootstrap methods. We investigate two general and practical scenarios. The first is the simultaneou...
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作者：Wikle, Christopher K.; North, Joshua; Gopalan, Giri; Yoo, Myungsoo
作者单位：University of Missouri System; University of Missouri Columbia; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; United States Department of Energy (DOE); Los Alamos National Laboratory; University of Texas System; University of Texas Austin
摘要：The recent success of deep neural network models with physical constraints (so-called, Physics-Informed Neural Networks, PINNs) has led to renewed interest in the incorporation of mechanistic information in predictive models. Statisticians and others have long been interested in this problem, which has led to several practical and innovative solutions dating back decades. In this overview, we focus on the problem of data-driven prediction and inference of dynamic spatio-temporal processes that...
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作者：Lee, Seong Jin; Sun, Will Wei; Liu, Yufeng
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Purdue University System; Purdue University; University of Michigan System; University of Michigan
摘要：Reinforcement learning from human feedback (RLHF) has become a cornerstone for aligning large language models with human preferences. However, the heterogeneity of human feedback, driven by diverse individual contexts and preferences, poses significant challenges for reward learning. To address this, we propose a Low-rank Contextual RLHF (LoCo-RLHF) framework that integrates contextual information to better model heterogeneous feedback while maintaining computational efficiency. Our approach b...
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作者：Wang, Ying; Ren, Mengye
作者单位：New York University
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作者：Cui, Yidan; Ma, Shiyang; Yuan, Yuxin; Zhu, Nengjie; Chen, Haifeng; Wei, Ting; Li, Zilin; Li, Xihao; Yu, Zhangsheng
作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Northeast Normal University - China; Northeast Normal University - China; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：The increasing availability of large-scale, population-based whole-genome sequencing (WGS) data enables comprehensive analyses of rare genetic variants, which are crucial for unraveling the genetic mechanisms underlying complex traits and diseases. Time-to-event traits offer the advantage of capturing both diagnosis status and timing, facilitating the identification of genetic variants associated with age of onset, disease progression, and lifespan. However, existing methods primarily focus on...
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作者：Chernozhukov, Victor; Newey, Whitney K.; Singh, Rahul; Syrgkanis, Vasilis
作者单位：Massachusetts Institute of Technology (MIT); Harvard University; Harvard University; Stanford University
摘要：Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as l...
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作者：Frazier, David T.; Nott, David J.
作者单位：Monash University; National University of Singapore; National University of Singapore
摘要：Modular Bayesian methods perform inference in models that are specified through a collection of coupled sub-models, known as modules. These modules often arise from modeling different data sources or from combining domain knowledge from different disciplines. Cutting feedback is a Bayesian inference method that ensures misspecification of one module does not affect inferences for parameters in other modules, and produces what is known as the cut posterior. However, choosing between the cut pos...
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作者：Xu, Yiqing; Zhao, Anqi; Ding, Peng
作者单位：Stanford University; Duke University; University of California System; University of California Berkeley
摘要：We formulate factorial difference-in-differences (FDID), a research design that extends canonical difference-in-differences (DID) to settings in which an event affects all units. In many panel data applications, researchers exploit cross-sectional variation in a baseline factor alongside temporal variation in the event, but the corresponding estimand is often implicit and the justification for applying the DID estimator remains unclear. We frame FDID as a factorial design with two factors, the...
