论文检索

动态协变量平衡：利用潜在结果局部投影估计跨期处理效应AI 解读

Abstract This article concerns the estimation and inference of treatment effects in panel data settings when treatments change dynamically over time. We propose a balancing method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on the trajectory of all past treatments; and (iii) heterogeneity of treatment effects. Our approach…

维度与样本量成比例时逻辑回归的 Diaconis–Ylvisaker 先验惩罚似然AI 解读

Summary We characterize the behaviour of the maximum Diaconis–Ylvisaker prior penalized likelihood estimator in high-dimensional logistic regression, where the number of covariates is a fraction $ \kappa\in(0,1) $ of the number of observations $ n $, as $ n\to\infty $. We construct a rescaled estimator with zero asymptotic aggregate bias, and define adjusted $ Z $-statistics and rescaled penalized likelihood ratio statistics that exhibit the typical null asymptotic…

不完全区组设计的设计型因果推断AI 解读

Summary Researchers often turn to block randomization to increase the precision of their inference or for practical reasons, such as in multi-site trials. However, if the number of treatments under consideration is large, it may not be feasible or practical to assign all treatments within each block. We develop novel inference results under the finite-population, design-based framework for natural alternatives to the complete block design that do not require reducing the…

用平均导数效应参数化连续处理的影响AI 解读

Summary The average treatment effect is commonly used to quantify the main effect of a binary treatment on an outcome. Extensions to continuous treatments are usually based on the dose-response curve or shift interventions, but both require strong overlap conditions, and the resulting curves may be difficult to summarize. This article focuses instead on average derivative effects, which are scalar estimands related to infinitesimal shift interventions requiring only local…

利用高斯过程推断流形AI 解读

Abstract It is often of interest to infer lower-dimensional structure underlying complex data. As a flexible class of nonlinear structures, it is common to focus on Riemannian manifolds. Most existing manifold-learning algorithms replace the original data with lower-dimensional coordinates without providing an estimate of the manifold or using it to denoise the original data. This article proposes a new methodology to address these issues, allowing interpolation of the…

样本与类别扰动下多类别数据的均值比估计AI 解读

Summay We consider the problem of estimating ratios of means of a multivariate outcome across covariates when the data are observed with unknown sample-specific and category-specific perturbations. Our model admits a partially identifiable estimand, and we establish full identifiability by imposing interpretable parameter constraints. To reduce bias and guarantee the existence of estimators in the presence of sparse observations, we apply an asymptotically negligible and…

利用代理控制结局的数据整合后推断AI 解读

Summary Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variation, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due to data-dependent processes. We introduce a robust post-integrated inference method that accounts for latent heterogeneity by leveraging control outcomes. Using causal interpretations, we…

校准敏感性模型AI 解读

Summary In causal inference, sensitivity models are used to assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter (which quantifies the degree of unmeasured confounding) is often difficult to interpret. For this reason, researchers sometimes compare the sensitivity parameter to an estimate of measured confounding, a process known as calibration or benchmarking. However, calibrated estimates are not always interpreted correctly, and…

点过程与随机场的谱估计AI 解读

Summary Spatial variables can be observed in many different forms, such as regularly sampled random fields (lattice data), point processes and randomly sampled spatial processes. Joint analysis of such collections of observations is clearly desirable, but complicated by the lack of an easily implementable analysis framework. We fill this gap by providing a multitaper analysis framework using coupled discrete and continuous data tapers, combined with the discrete Fourier…

利用投影乘积进行多视图子空间学习的谱框架AI 解读

Summary Multi-view data provide complementary information on the same set of observations, with multi-omics and multimodal sensor data being common examples. Analysing such data typically requires distinguishing between shared (joint) and unique (individual) signal subspaces from noisy, high-dimensional measurements. Despite many proposed methods, the conditions for reliably identifying joint and individual subspaces remain unclear. We rigorously quantify these conditions,…

匹配估计量的自助法一致性AI 解读

Summary In a landmark paper, Abadie & Imbens (2008)showed that the naive bootstrap is inconsistent when applied to nearest neighbour matching estimators of the average treatment effect with a fixed number of matches. Since then, this finding has inspired numerous efforts to address the inconsistency issue, typically by employing alternative bootstrap methods. In contrast, in this work we show that the naive bootstrap is provably consistent for the original matching…

通过成对似然截断估计高维协方差AI 解读

Summary Pairwise likelihood is an approximation of the full likelihood function that facilitates the analysis of high-dimensional covariance models. By combining marginal bivariate likelihoods, it effectively simplifies high-dimensional dependencies, making the estimation process more manageable. We introduce estimation of sparse high-dimensional covariance matrices by maximizing a truncated version of the pairwise likelihood function, obtained by including pairwise terms…

存在不依从时二水平多因素实验的因果效应界AI 解读

Summary Factorial experiments are ubiquitous in the social and biomedical sciences, but when units fail to comply with each assigned factor, identification and estimation of the average treatment effects become impossible without strong assumptions. Leveraging an instrumental variables approach, previous studies have shown how to define and estimate the causal effect of treatment uptake among respondents who comply with treatment. A major caveat is that these results rely on…

稀疏高阶偏最小二乘：变量选择、降维与张量去噪AI 解读

Abstract Motivated by the challenge of estimating effects of DNA methylation on 3D genomic contacts captured by multimodal single-cell Hi-C data, we consider the tensor-response partial least squares model with $ \mathcal{Y}=\mathcal{B}\times_{1}X+\mathcal{F} $, where the correlated high-dimensional predictors $ X\in\mathbb{R}^{n\times d_{1}} $ and the sparse and noisy high-dimensional responses $ \mathcal{Y}\in\mathbb{R}^{n\times\prod_{m}d_{m}} $ are observed, the low-rank…

函数型数据的极端相关系数AI 解读

Abstract We propose a coefficient that measures extremal dependence in paired samples of functions. It has properties similar to the Pearson correlation, but differs in two significant ways: (i) it is designed to measure dependence between curves; and (ii) it focuses only on extreme curves. The new coefficient is derived within the framework of regular variation in Banach spaces. A consistent estimator is proposed and justified by an asymptotic analysis and a simulation…

非参数加性回归中交互效应的识别与估计AI 解读

Abstract A new formulation of the additive interaction model is introduced. In contrast to existing approaches, the new formulation separates well the joint effects of covariates that cannot be accounted for by individual main effects. The new approach enables correct interpretation of interaction effects by making them orthogonal to the associated main effects in the $ L^{2} $ sense. A new method is developed to estimate the resulting main and interaction effects.…

因果发现后因果效应的选择后推断AI 解读

Summary Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estimation of causal effects (e.g., average treatment effects) when there is uncertainty about which covariates ought to be adjusted for, or which variables act as confounders versus mediators. However, naively using the data…

空间自混杂：空间回归中与平滑度有关的估计偏差AI 解读

Abstract The estimation of regression parameters in spatially referenced data plays a crucial role across various scientific domains. A common approach involves employing an additive regression model to capture the relationship between observations and covariates, accounting for spatial variability not explained by the covariates through a Gaussian random field. We study the effect of misspecified covariates, in particular when the misspecification changes the smoothness. We…

未知协方差下的高斯信息分解AI 解读

Abstract Common workflows in machine learning and statistics rely on the ability to partition the information in a dataset into independent portions. Recent work has shown that this may be possible even when conventional sample splitting is not, such as when the number of samples, $ n $, is one or when observations are not independent and identically distributed. In the case of multivariate Gaussian data, these alternatives to sample splitting require knowledge of the…

近端间接治疗比较AI 解读

Summary We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in one randomized controlled trial, but available in the other. The former is the target trial, and the latter is the source trial. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions. However, it is a common concern whether all relevant effect modifiers are measured and controlled for. We…

局部因果发现中的结构限制：识别目标变量的直接原因AI 解读

Summary We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs that represent the causal structure is a fundamental problem in science. Several results are known when the full directed acyclic graph is identifiable from the distribution, such as when a nonlinear Gaussian data-generating process is assumed. Here, we are interested only in identifying the direct causes of one…

空间自回归模型的稳健估计AI 解读

This article proposes and studies two Huber-type estimation approaches, namely, the Huber instrumental variable (IV) estimation and the Huber generalized method of moments (GMM) estimation, for a spatial autoregressive model. We establish the consistency, asymptotic distributions, finite sample breakdown points, and influence functions of these estimators. Simulation studies show that compared to the corresponding traditional estimators (the two-stage least squares…

函数系数回归与气候敏感性分析的新渐近理论AI 解读

A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long-range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and…

Abstract 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…