2024

最新文章

• Parameterizing and simulating from causal models AI翻译
  作者：Seaman, Shaun R.
  作者单位：University of Cambridge; MRC Biostatistics Unit
  摘要：Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a ...
• Steven R Howard's contribution to the Discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas AI翻译
  作者：Howard, Steven R.
• Philip S Thomas, Erik Learned-Miller and My Phan's contribution to the Discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas AI翻译
  作者：Thomas, Philip S.; Learned-Miller, Erik; Phan, My
  作者单位：University of Massachusetts System; University of Massachusetts Amherst
• Ben Swallow's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes and Walker AI翻译
  作者：Swallow, Ben
  作者单位：University of St Andrews; University of St Andrews; University of St Andrews
• Policy evaluation for temporal and/or spatial dependent experiments AI翻译
  作者：Luo, Shikai; Yang, Ying; Shi, Chengchun; Yao, Fang; Ye, Jieping; Zhu, Hongtu
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of London; London School Economics & Political Science; Peking University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：The aim of this article is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-temporal Varying Coefficient Decision Process model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of the average treatment effec...
• Non-agency interventions for causal mediation in the presence of intermediate confounding AI翻译
  作者：Diaz, Ivan
  作者单位：New York University
  摘要：Recent approaches to causal inference have focused on causal effects defined as contrasts between the distribution of counterfactual outcomes under hypothetical interventions on the nodes of a graphical model. In this article, we develop theory for causal effects defined with respect to a different type of intervention, one which alters the information propagated through the edges of the graph. These information transfer interventions may be more useful than node interventions in settings in w...
• The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables AI翻译
  作者：Agrawal, Raj; Squires, Chandler; Prasad, Neha; Uhler, Caroline
  作者单位：Massachusetts Institute of Technology (MIT); Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
  摘要：Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particula...
• Debiased inference for a covariate-adjusted regression function AI翻译
  作者：Takatsu, Kenta; Westling, Ted
  作者单位：Carnegie Mellon University; University of Massachusetts System; University of Massachusetts Amherst; Carnegie Mellon University
  摘要：In this article, we study nonparametric inference for a covariate-adjusted regression function. This parameter captures the average association between a continuous exposure and an outcome after adjusting for other covariates. Under certain causal conditions, it also corresponds to the average outcome had all units been assigned to a specific exposure level, known as the causal dose-response curve. We propose a debiased local linear estimator of the covariate-adjusted regression function and d...
• Bayesian model selection via mean-field variational approximation AI翻译
  作者：Zhang, Yangfan; Yang, Yun
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：This article considers Bayesian model selection via mean-field (MF) variational approximation. Towards this goal, we study the non-asymptotic properties of MF inference that allows latent variables and model misspecification. Concretely, we show a Bernstein-von Mises (BvM) theorem for the variational distribution from MF under possible model misspecification, which implies the distributional convergence of MF variational approximation to a normal distribution centring at the maximal likelihood...
• Image response regression via deep neural networks AI翻译
  作者：Zhang, Daiwei; Li, Lexin; Sripada, Chandra; Kang, Jian
  作者单位：University of Pennsylvania; University of California System; University of California Berkeley; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex...