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存在不依从时阶梯楔形设计的随机化推断：姑息治疗试验应用AI 解读

While palliative care is increasingly commonly delivered to hospitalized patients with serious illnesses, few studies have estimated its causal effects. Courtright et al. (2016) adopted a stepped-wedge cluster-randomized design to assess the effect of palliative care on a patient-centered outcome. The randomized intervention was a nudge to administer palliative care but did not guarantee receipt of palliative care, resulting in noncompliance. A subsequent analysis using…

稀疏市场—产品冲击下的离散选择需求模型估计AI 解读

When credible instruments are scarce or estimates hinge on instrument choice, an alternative to the leading Berry--Levinsohn--Pakes (BLP) approach to demand estimation with aggregate data can be useful. We propose a Bayesian random-coefficients discrete-choice demand estimator that jointly recovers preference parameters and market--product demand shocks under a sparsity restriction, without requiring instrumental variables. Shrinkage priors select a sparse set of active…

高光谱解混的乘性变量误差模型AI 解读

Estimating endmember spectra and their corresponding abundances from hyperspectral images is a fundamental inverse problem in remote sensing. The standard linear mixing model relies on additive homoscedastic errors, which misspecifies the mean-variance relationship inherent to optical measurements for which the noise intensity might be proportional to the signal magnitude. This leads to confidence intervals with poor coverage. To address these limitations, we propose a…

不规则历史下纵向因果推断的双重稳健函数型表示学习AI 解读

Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times. Standard doubly robust estimators usually require scalar summaries, whereas sequence learners optimize prediction losses that need not stabilize the efficient influence function. We propose Doubly Robust Functional Representation Learning (DR-FRL), a cross-fitted…

流形值时间序列的黎曼因子模型AI 解读

We propose a Riemannian factor model (RFM), a novel framework for analyzing potentially high-dimensional time series data observed on Riemannian manifolds. Such time series are encountered in various applications, including economics, finance, medical imaging, and genomics and microbiome research. The proposed model is geometry-aware and accounts for the inherent nonlinearity in the data. In a high-dimensional asymptotic regime, where the manifold dimension is allowed to…

工具变量估计的新方法：TEAM-IVAI 解读

Instrumental-variable (IV) analyses can be undermined when some instruments violate the exclusion restriction through direct effects on the outcome. Existing robust IV methods, including sisVIVE and CIIV, rely on majority- or plurality-validity conditions. We propose TEAM-IV, which targets joint validity by identifying sets of instruments that appear valid together and aggregating them, allowing reliable estimation even when only a small number of candidate instruments are…

含干扰参数的矩约束统一推断AI 解读

This paper proposes a simple unified inference approach on moment restrictions in the presence of nuisance parameters. The proposed test is constructed based on a new characterization that avoids the estimation of nuisance parameters and can be broadly applied across diverse settings. Under suitable conditions, the test is shown to be asymptotically size controlled and consistent for both independent and dependent samples. Monte Carlo simulations show that the test performs…

通过预过滤筛式自助法校正函数型时间序列长记忆估计偏差AI 解读

We investigate a bias correction procedure based on sieve bootstrapping to estimate the long-memory parameter d in stationary or nonstationary fractionally integrated processes. The resampling method implements a sieve bootstrap method on data prefiltered by a preliminary estimate of the long-memory parameter. For the initial estimate, we recommend the local polynomial Whittle with noise (LPWN) estimator in Frederiksen et al. (2012) to reduce bias, especially in the presence…

拓扑因果数据分析的数学框架AI 解读

Many modern outcomes, including images, point clouds, networks, and spatial fields, are structured objects for which \(Y^1-Y^0\) may be undefined or scientifically inadequate. We introduce \emph{Topological Causal Data Analysis} (TCDA), a framework separating the observation space, causal-model class, topological representation, and causal query. Topology does not define interventions; it supplies stable, shape-sensitive summaries after causal assumptions have been…

Sinkhorn 哈密顿蒙特卡洛：用于熵正则最优传输广义贝叶斯AI 解读

Bayesian posterior sampling is a ubiquitous paradigm for problems where a point estimate of parameters is not sufficient, such as risk analysis and uncertainty quantification. However, likelihoods may be misspecified, intractable, computationally expensive, or not representative of the discrepancy of interest. Generalized Bayes extends likelihood-based posterior updates by using other losses. Sinkhorn divergences have appealing geometric properties: they compare empirical…

时变相关性推断的渐近一致错误发现率控制AI 解读

Inference for locally stationary time series is challenging because the associated hypotheses form an uncountable collection over a continuous time interval, making pointwise false discovery rate (FDR) control inadequate for simultaneous statistical guarantees. We introduce a novel asymptotically uniform false discovery rate (AuFDR), defined as the expectation of the $L_r$-norm of the false discovery proportion (FDP) process where $r$ is allowed to diverge, to quantify and…

上确界型统计量的实质性变化自归一化推断AI 解读

We develop a selfnormalized approach to inference for relevant changes in functional time series measured by the supremum norm. The main difficulty is that the supremum norm is not Hadamard differentiable, so standard projection-based selfnormalization does not apply and the limiting distribution may depend on the geometry of the extremal set and the long-run covariance structure. We address this problem by replacing the supremum norm with a smooth log-sum-exp approximation…

使用收缩估计量的自适应实验设计AI 解读

In multi-armed trials, adaptive designs are a popular way to increase estimation efficiency or identify optimal treatments. Several recent papers have proposed adaptive variants of the classical Neyman allocation to assign treatments in sequential trials, with the goal of minimizing the error of a Horvitz-Thompson-style estimator. However, this approach may be inefficient, because it fails to borrow information across the treatment arms. In this paper, we consider adaptivity…

用因果贝叶斯网络与赖欣巴赫原则重新审视格兰杰因果AI 解读

Granger causality (GC) is widely used to infer directed relationships in time-series data. However, its predictive criterion does not by itself distinguish direct causal effects from dependencies induced by common causes, indirect paths, collider conditioning, or model misspecification. We revisit this limitation by interpreting bivariate and multivariate GC through causal Bayesian networks and Reichenbachs common cause principles. Under explicit graphical assumptions,…

非线性面板数据模型的低秩估计AI 解读

This paper investigates nonlinear panel models with interactive fixed effects and introduces a general framework for parameter estimation under potentially nonconvex objective functions. We propose a computationally feasible two-step estimation procedure. In the first step, nuclear-norm regularization (NNR) is used to obtain preliminary estimators of the coefficients of interest, factors, and factor loadings. The second step involves an iterative procedure for post-NNR…

未测混杂下获得统计支持的错误因果解释如何渐近出现AI 解读

Unmeasured confounding is widely recognized as a limitation of observational causal inference, but its implications for data-driven causal discovery are often understated. We provide an asymptotic characterization of this limitation. When the true causal variable is unobserved but correlated proxy variables are available, increasing sample size while searching for increasingly stable, data-driven causal structures will make associations involving observable proxies become…

监管试验协变量调整的良好实践：从固定到数据自适应方法AI 解读

While randomization justifies the use of unadjusted effect estimators in randomized trials, there is growing interest in covariate adjustment to improve precision. Adjusting for baseline variables that are prognostic of the outcome can reduce estimator variance, resulting in narrower confidence intervals and increased statistical power. Recent guidance by the U.S. Food and Drug Administration supports fixed adjustment for prognostic covariates using parametric regression…

最优因果标注：社会服务个案记录的应用AI 解读

Problem definition: Estimating causal effects of interventions is central to policy and operations, but outcome data are often missing or costly to obtain. LLMs can provide text annotation at scale but may be subject to unknown bias. When ground-truth outcomes require expensive expert labeling or follow-up, budget limits typically allow only a fraction of the data to be labeled. Motivated by collaboration with a nonprofit conducting street outreach in homelessness services,…

工具变量回归中的简便稳健程序AI 解读

A common concern in empirical modelling centres around whether estimated regression coefficients are affected by a small set of outlying observations. To conduct outlier robustness checks in practical applications of instrumental variables regressions, the common practice is to run ordinary two stage least squares (2SLS) and remove observations with standardised residuals beyond a chosen cut-off value. Subsequently, the trimmed 2SLS is computed and compared to the original…

用 BAND 缓解维度灾难：高维非参数分布估计AI 解读

Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality. We propose a sparse Bayesian network approach in which each conditional probability is estimated using sparsity-aware conditional mean methods. The resulting estimator, \textit{BAyesian Network Distribution regression} (BAND), handles mixed data types in high-dimensional time series and achieves polynomial total variation convergence rates while allowing the…

高斯图模型下网络复杂度的估计AI 解读

The proportion of edges in a Gaussian graphical model (GGM) characterizes the complexity of its conditional dependence structure. Since edge presence corresponds to a nonzero entry of the precision matrix, estimation of this proportion can be formulated as a large-scale multiple testing problem. We propose an estimator that combines p-values from simultaneous edge-wise tests, conducted under false discovery rate control, with Storey's estimator of the proportion of true null…

稳健插值分位数估计量：渐近理论与效率AI 解读

This paper introduces a unified family of interpolated quantile estimators obtained by augmenting the check loss with quadratic, Huber, or Tukey's bisquare regularization. The estimators are indexed by the quantile level $τ$ and an interpolation parameter $h$. They reduce to the classical empirical quantile when $h=0$, while increasing $h$ continuously shifts the effective probability level toward the center of the distribution. A complete asymptotic theory is developed. For…

变分贝叶斯稀疏负二项回归AI 解读

Count data with overdispersion and high-dimensional predictors pose significant challenges in modern applications. While negative binomial regression offers a flexible modeling framework, existing Bayesian approaches rely on computationally expensive MCMC methods that become impractical in high-dimensional settings. This paper develops a variational Bayesian framework for sparse negative binomial regression using horseshoe and continuous spike-and-slab priors. Our proposed…

DoTime：干预与反事实时间序列的合成基准生成器AI 解读

Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science. We introduce \textbf{DoTime}, an open, scalable, and theoretically grounded generator of multivariate temporal structural causal models (TSCMs) with interventions, released as the \code{dotime} PyPI package together…