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暴露或结局罕见时哪种正则化倾向得分与双稳健方法校准最佳？基于代理混杂调整的plasmode研究AI 解读

Purpose. Confounding adjustment in health-care database studies screens large proxy libraries where events per variable are low, straining standard propensity score (PS) methods, and many regularized variable-selection strategies exist (outcome-adaptive LASSO [OAL], group LASSO/GLiDeR, highly adaptive LASSO [HAL]). Yet few comparisons have varied exposure prevalence within such a selection menu, and none pairs it with doubly robust estimation, compute accounting, and a…

未观测混杂下的政策学习：稳健且有效的方法AI 解读

This paper develops a robust and efficient method for policy learning from observational data in the presence of unobserved confounding, complementing existing instrumental variable (IV) based approaches. We employ the marginal sensitivity model (MSM) to relax the commonly used yet restrictive unconfoundedness assumption by introducing a sensitivity parameter that captures the extent of selection bias induced by unobserved confounders. Building on this framework, we consider…

阶梯楔形集群随机试验中的模型稳健标准化AI 解读

Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, enabling all clusters to receive the intervention through a staggered rollout. Traditional model-based methods, including generalized estimating equations and mixed models, yield estimates that depend on implicit weighting schemes and parametric assumptions, and therefore may target ambiguous estimands under model misspecification. In this article, we propose a…

通过混合分布学习处理分类未观测混杂的因果推断AI 解读

Unobserved confounding is a fundamental challenge for estimating causal effects. To address unobserved confounding, recent literature has turned to two different approaches -- proxy variables and the use of multiple treatments. The first approach, commonly referred to as proximal causal inference, requires proxies to be assigned to specific asymmetric roles: treatment-inducing proxies (negative control exposures), variables that act as common causes of the treatment and…

基于二次型的重随机化统一框架AI 解读

When designing a randomized experiment, one way to ensure treatment and control groups exhibit similar covariate distributions is to randomize treatment until some prespecified level of covariate balance is satisfied; this strategy is known as rerandomization. Most rerandomization methods utilize balance metrics based on a quadratic form $\mathbf{v}^T \mathbf{A} \mathbf{v}$, where $\mathbf{v}$ is a vector of covariate mean differences and $\mathbf{A}$ is a positive…

谱密度矩阵的多元贝叶斯 P 样条估计及 LISA TDI 噪声应用AI 解读

We present a Bayesian P-spline method for estimating the frequency-dependent cross-spectral density matrix of stationary multivariate time series. The inverse spectral matrix is parametrised through its frequency-varying Cholesky decomposition, which guarantees Hermitian positive definiteness at every frequency. Each real log-diagonal entry and each real and imaginary off-diagonal entry is given an independent penalised B-spline prior that controls smoothness. Inference uses…

主分层下异质性处理效应的双重交叉拟合去偏机器学习AI 解读

Principal stratification provides a foundational framework for causal inference with intermediate outcomes by defining causal effects within subpopulations, yet existing work has largely focused on average effects across strata rather than treatment effect heterogeneity within strata. Such within-stratum heterogeneity informs individualized treatment decisions but the associated methods are sparse. We address this gap by studying the identification and estimation of the…

MAPLE：基于 Mapper 的局部预测与数据驱动覆盖选择AI 解读

High dimensional biomedical data often exhibit nonlinear, heterogeneous, and manifold driven structures that challenge global parametric and tree-based models. We propose MAPLE (mapper-based Adaptive Prediction via Local Estimation), a localized prediction framework grounded in topological data analysis. The method is formulated as a nonparametric estimator of conditional class probabilities that adapts to the intrinsic geometry of the predictor space. Neighborhoods are…

正视溢出：实验中的空间效应AI 解读

Interventions delivered in space generate effects that spill over between experimental units. We develop a framework for spatial experiments in which causal estimands, inclduing direct, indirect, total, and dose--response effects, are linear functionals of a spillover kernel. Under cluster randomisation with a fixed exposure set size, the data identify the shape of the kernel but not its level: the level is aliased with the intercept and direct effect, at any sample size and…

因果归因中必要性概率的统计推断AI 解读

To answer questions of "causes of effects", the probability of necessity was previously introduced for assessing whether an observed outcome was caused by an earlier treatment. However, statistical inference for the probability of necessity is understudied due to several difficulties, which hinder its application in practice. The evaluation of the probability of necessity involves the joint distribution of potential outcomes, and thus it is generally not point identified and…

兼容机器学习的处理效应异质性综合检验AI 解读

This study proposes a formal, computationally efficient nonparametric omnibus test for treatment-effect heterogeneity that is compatible with a broad class of estimators, including modern machine-learning methods. The test is designed for settings in which identification can rely on high-dimensional controls while heterogeneity is assessed with respect to a low-dimensional subset of covariates. We derive the test statistic's asymptotic null distribution and develop a…

因子增强的机器学习面板回归AI 解读

This paper develops the asymptotic theory for high-dimensional panel data regressions in settings with cross-sectionally dependent errors driven by common shocks. We consider a factor-augmented sparse-group LASSO estimator that combines MIDAS aggregation with latent factors. The estimator can take advantage of the mixed-frequency group structure in the time-series dimension. Theory shows that it can outperform the standard LASSO estimator both for prediction and estimation…

正态均值估计中收缩阈值规则的近似风险最小化AI 解读

We develop an approximate risk minimization framework for shrinkage-thresholding estimation in normal mean problems. In the canonical multivariate normal mean model, we introduce a general functional class of estimators that contains classical shrinkage and thresholding behavior, including James-Stein-type and lasso-type rules. We express quadratic risk as a functional over this class, derive optimality conditions for both oracle risk and data-driven approximate risk…

半参数效率理论：概率分布空间上的微分学AI 解读

Semiparametric efficiency theory provides the mathematical foundation for influence-function-based estimation, including one-step estimators, targeted minimum loss estimators, and many modern inferential methods used in causal inference and missing data analysis. Despite its widespread use, the theory is often presented through a collection of technical constructions whose geometric meaning remains opaque. As a result, influence functions are often derived and applied…

矩阵值时间序列共同变动的分解：拟结构降秩方法AI 解读

A pseudo-structural framework is proposed for analyzing contemporaneous co-movements in stationary reduced-rank matrix autoregressive (RRMAR) models. Unlike conventional vector autoregressive (VAR) models that discard the matrix structure, the formulation preserves it, enabling a decomposition of co-movements into three interpretable components: row-specific, column-specific, and joint (row--column) interactions across the matrix-valued time series. The estimator admits…

高维逻辑回归的普适性与依赖结构下的新CGMT及其在数据增强中的应用AI 解读

Over the last decade, a wave of research has characterized the exact asymptotic risk of many high-dimensional models in the proportional regime. Two foundational results have driven this progress: Gaussian universality, which shows that the asymptotic risk of estimators trained on non-Gaussian and Gaussian data is equivalent, and the convex Gaussian min-max theorem (CGMT), which characterizes the risk under Gaussian settings. However, these results rely on the assumption…

利用随机算法生成的数据估计因果效应AI 解读

Recommendation systems and chatbots present content to users, typically using stochastic algorithms that select the content based on user characteristics or context. Examples of content include chat responses, videos, or items available for purchase. Scientists and application developers are often interested in whether characteristics of content increase outcomes such as user engagement. Estimates of such causal effects may guide content providers to generate content that…

二元结局处理效应的最优实验设计与稳健推断AI 解读

We study randomized experiments with binary outcomes under Neyman's nonparametric model, where covariate measurements are fixed but potential outcomes are random. In this setting we derive the exact variance of the difference-in-means estimator and characterize designs that minimize it. We show that any balanced design satisfying a covariate-balance condition is asymptotically optimal, and we prove that a broad class of blocking designs satisfies this condition under mild…

均值需估计时 L-mixing 时间序列经典谱估计量的非渐近分析AI 解读

Spectral estimation is an important tool in time series analysis, with applications including economics, astronomy, and climatology. The asymptotic theory for non-parametric estimation is well-known but the development of non-asymptotic theory is still ongoing. Our recent work obtained the first non-asymptotic error bounds on the Bartlett and Welch methods with restrictive assumptions. In this work, we derive non-asymptotic error bounds for both Bartlett and Welch estimators…

营销干预中贝叶斯因果影响模型的敏感性与早期检测AI 解读

Marketing systems frequently undergo operational changes that may affect performance, making timely detection of adverse effects essential for decision making. While Bayesian Causal Impact models are widely used to estimate the causal effects of interventions, their ability to support early operational monitoring remains less explored. This paper proposes a simulation based framework to evaluate the sensitivity and detection capabilities of Bayesian causal impact analysis…

线性回归中的潜在权重与隐含因果设计AI 解读

Applied researchers routinely use linear regression to estimate causal effects, justified by quasi-experimental treatment variation, while leaving assumptions on treatment assignment implicit. We formalize a minimal criterion for quasi-experimental interpretation -- that the regression estimates some contrast of potential outcomes under the true assignment process, regardless of potential outcomes -- and characterize its implications for arbitrary regressions. This criterion…

高维下广义回归估计量的方差估计AI 解读

In survey sampling, the goal is to estimate finite population parameters such as totals, means, and proportions. At the estimation stage, it is common to have access to auxiliary information in the form of covariates known either in aggregate form or for each population unit. These covariates are often used, through models relating them to the variable of interest, to improve efficiency; this approach is known as model-assisted estimation. Modern applications increasingly…

政权更替的实证全局博弈模型AI 解读

Global games theory provides a tractable framework for analyzing coordination problems with multiple equilibria, with regime overthrow serving as a canonical application. A large empirical literature on coups d'état examines the relationship between country-level characteristics, coup occurrence, and coup success using reduced-form approaches that leave the underlying coordination problem implicit. Bridging these literatures, we develop an estimable global games model of…

用随机镜像上升近似极小极大估计有界正态均值AI 解读

This paper presents a computational approach to find an approximately minimax estimator for the classical Bounded Normal Mean problem. The suggested procedure is the Bayes estimator corresponding to an approximately least-favorable distribution obtained from a stochastic mirror ascent routine for concave maximization. The paper shows that both the approximately least-favorable distribution and the approximately minimax estimator are indeed close (in a sense we make precise)…