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作者：Yu, Ruoqi; Karmakar, Bikram; Vandeleest, Jessica; Schwarz, Eleanor Bimla
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Wisconsin System; University of Wisconsin Madison; University of California System; University of California Davis; University of California System; University of California San Francisco
摘要：Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Randomized controlled trials (RCTs) are considered the gold standard for internal validity of inferences, whereas observational studies (OSs) often provide the opportunity for greater external validity. However, both data sources have inherent limitations preventing their use for broadly valid statistical inferences: RCTs may lack generalizability due to their selective eligibi...
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作者：Luedtke, Alex
作者单位：University of Washington; University of Washington Seattle
摘要：We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influence function. Dimple avoids this task by applying automatic differentiation to the statistical functional of interest. Doing so requires exp...
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作者：Singh, Rahul; Iliopoulos, George; Davidov, Ori
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi; University of Piraeus; University of Haifa
摘要：Least square estimators for graphical models for cardinal paired comparison data with and without covariates are rigorously analysed. Novel, graph-based, necessary, and sufficient conditions that guarantee strong consistency, asymptotic normality, and the exponential convergence of the estimated ranks are emphasized. A complete theory for models with covariates is laid out. In particular, conditions under which covariates can be safely omitted from the model are provided. The methodology is em...
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作者：Fischer, Lasse; Ramdas, Aaditya
作者单位：University of Bremen; Carnegie Mellon University
摘要：In a Monte Carlo test, the observed dataset is fixed, and several resampled or permuted versions of the dataset are generated in order to test a null hypothesis that the original dataset is exchangeable with the resampled/permuted ones. Sequential Monte Carlo tests aim to save computational resources by generating these additional datasets sequentially one by one and potentially stopping early. While earlier tests yield valid inference at a particular prespecified stopping rule, our work devel...
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作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：In an observational block design, there are I blocks of J individuals, typically with one treated individual and J-1 controls; however, unlike a randomized block design, individuals were not randomly assigned to treatment or control. To be convincing, an observational block design must demonstrate that an ostensible treatment effect is not actually a consequence of small or moderate unmeasured biases of treatment assignment in the absence of a treatment effect. It is known that weighting to ig...
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作者：Xu, Zhiwei; Gan, Ziming; Zhou, Doudou; Shen, Shuting; Lu, Junwei; Cai, Tianxi
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作者：Duarte, Eliana; Solus, Liam
作者单位：Universidade do Porto; Royal Institute of Technology
摘要：We address the problem of representing context-specific causal models based on both observational and experimental data collected under general (e.g. hard or soft) interventions by introducing a new family of context-specific conditional independence models called CStrees. This family is defined via a novel factorization criterion that allows for a generalization of the factorization property defining general interventional directed acyclic graph (DAG) models. We derive a graphical characteriz...
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作者：Bucher, Axel; Staud, Torben
作者单位：Ruhr University Bochum
摘要：The block maxima method is a standard approach for analyzing the extremal behaviour of a potentially multivariate time series. It has recently been found that the classical approach based on disjoint block maxima may be universally improved by considering sliding block maxima instead. However, the asymptotic variance formula for estimators based on sliding block maxima involves an integral over the covariance of a certain family of multivariate extreme value distributions, which makes its esti...
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作者：Borgonovo, Emanuele; Figalli, Alessio; Ghosal, Promit; Plischke, Elmar; Savare, Giuseppe
作者单位：Bocconi University; Bocconi University; ETH Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Chicago; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
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作者：Shi, Jianwei; Abdulah, Sameh; Sun, Ying; Genton, Marc G.
作者单位：King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
摘要：Advancements in information technology have enabled the creation of massive spatial datasets, driving the need for scalable and efficient computational methodologies. Although offering viable solutions, centralized frameworks are limited by vulnerabilities such as single-point failures and communication bottlenecks. This paper presents a fully decentralized framework tailored for parameter inference in spatial low-rank models to address these challenges. A key obstacle arises from the spatial ...
