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双向不可观测异质性的非参数识别AI 解读

We study identification of two-way unobserved heterogeneity in the nonparametric panel regression $G_{it}=g(α_i,γ_t)+\varepsilon_{it}$, where identification of the latent types reduces to constructing identified, \emph{injective} proxies for them. To this end we consider the singular value decomposition (SVD) of the bivariate regression function $g(α,γ)$ on a product domain $Ω_α\timesΩ_γ$, whose left singular functions $\{u_r\}$ serve as proxies for the unobserved…

基于因果图的方差分解：何时解释方差意味着因果作用AI 解读

Recursive application of the law of total variance decomposes the marginal variance of an outcome into components attributed to explanatory variables and a residual component. The resulting decomposition depends on the chosen conditioning order, and its components do not in general have causal interpretations. We develop a graph-based framework for defining causal counterparts of ordered variance components and establishing their identification from observed data. Under…

从风险预测到风险机制：道路安全与车险的多分辨率因果表示AI 解读

Operational risk models can estimate event frequencies precisely while leaving the underlying mechanisms weakly resolved. We study this resolution mismatch using motor insurance and road safety. A revisable DAG represents trip-level crash generation; a separate predictive layer links annual rating information to latent driving states; and an observation process maps crashes into recorded liability claims. Compatibility sets collect the structural and crash-to-claim laws that…

含未知趋势与周期成分的球面嵌入时间序列AI 解读

Spherically embedded time series are time series with values naturally residing on or can be equivalently mapped to the sphere. Despite their ubiquity in diverse scientific fields, these data frequently exhibit complex non-stationarity driven by latent trend and periodic components. Traditional Euclidean time series methods fail to account for the intrinsic non-Euclidean geometry of the sphere, leaving a critical gap in rigorous methodologies for modelling and forecasting…

受试者内实验的分析：识别、检验与敏感性AI 解读

Recent work encourages political scientists to move from post-only toward within-subject designs for improved precision from repeated measurements. We formalize a potential-outcomes framework for two-period within-subject designs that allows for unequal allocation and heterogeneous treatment and carryover effects. We characterize the pooled estimator and evaluate the carryover test used to justify pooling. We find: first, pooling identifies the average treatment effect only…

DIRECT：分解视觉内容分析中的受众偏好与创作效应AI 解读

Which visual choices make a post perform better? A growing literature answers this question with pooled coefficients estimated across many creators, which platforms translate into creative recommendations. We show that these coefficients blend two distinct patterns that can point in opposite directions for the same attribute. The first, audience preference, arises because creators who favor a style attract differently composed audiences, so their posts perform differently…

提高检验功效的统一自适应富集设计AI 解读

Randomized controlled trials (RCTs) are the gold standard for evaluating treatment effects, but fixed eligibility criteria and enrollment decisions can be inefficient, especially when treatment effects vary across patient subpopulations. Adaptive enrichment trials update enrollment using interim data to improve efficiency. Enrichment methods are developed for two settings: prespecified subgroups, and continuous covariates where enrollment is guided by a learned cutoff. Many…

高维网络数据的统计推断AI 解读

We develop a novel method of inference for network-dependent high-dimensional random vectors. Dependence is characterized via a functional dependence measure based on graph distance, allowing the approximation theory to capture the interaction between the decay of dependence and the growth of network neighborhoods. We establish Gaussian approximation results for the maximum norm under finite-moment and sub-Weibull conditions, providing explicit conditions under which the…

空间相关函数数据的小波贝叶斯层级模型与正则化马蹄先验AI 解读

Environmental monitoring networks increasingly record pollutant levels as curves observed over time at fixed stations. Yet, exposure and risk assessment often require predicting these curves, with their uncertainty, at unmonitored locations. Conventional smooth-basis methods for such spatially correlated functional data, including spline-based kriging, tend to over-smooth acute, high-frequency episodes such as pollution peaks. We propose a parsimonious wavelet-based Bayesian…

混合对照试验的功效与样本量计算AI 解读

Hybrid controlled trials (HCTs) augment randomized controls with external controls (ECs) to address practical challenges in randomized controlled trials (RCTs) and improve statistical power in settings such as rare diseases, oncology, and pediatrics. However, prospective sample-size determination is challenging because the required RCT sample size depends on the comparability of ECs with RCT controls, which are unavailable at the planning stage. We propose a 5+3 design for…

处理—混杂反馈下电力需求的边际结构模型与连续处理自适应 LASSOAI 解读

Temperature is the most important meteorological driver of electricity demand, but its causal effect has not been estimated under the time-varying confounding that characterizes weather processes. Current temperature is associated with weather conditions, including precipitation, snow cover, and cloud cover, that also influence electricity demand, and past temperature shapes future weather, producing treatment-confounder feedback under which standard regression adjustment is…

正则化高维加性张量自回归模型AI 解读

High-dimensional time series has diverse applications in econometrics and finance. Recent models for capturing temporal dependence have employed a bilinear representation for matrix time series, or the Tucker-decomposition based representation in case of tensor time series. A Tucker-decomposition based temporal effect is difficult to interpret on many occasions, along with its computational complexity due to the non-convex nature of the underlying optimization problem.…

精细分层再随机化设计AI 解读

Finely stratified randomization makes unadjusted treatment effect estimation efficient when matches are tight. However, match quality deteriorates rapidly as covariate dimension increases, attenuating the gains from stratification. Motivated by this problem, we study designs that finely stratify on a few important covariates, then rerandomize within matched groups to balance the remainder. We derive the asymptotic distribution for GMM estimators of general causal parameters…

FLEX-CP-DT：计数终点与时间趋势下的期中无效性分析AI 解读

Many large-scale phase III trials with recurrent event endpoints include a pre-planned interim analysis to evaluate early futility. Conditional power (CP), which quantifies the probability of achieving statistical significance at the final analysis given the interim data, is a commonly used tool to support such decisions. The standard negative binomial model with an offset term, widely adopted for analyzing recurrent events, implicitly assumes that event rates and treatment…

PLRD：部分线性断点回归推断AI 解读

Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that the widely used approaches to building confidence intervals in regression discontinuity designs often exhibit suboptimal behavior in practice. We propose a new estimator, the partially linear regression discontinuity (PLRD) estimator that, in set of a simulation studies carefully calibrated to twelve high-profile applications of regression…

聚类与弱依赖数据下 U 统计量的极限理论AI 解读

This paper develops asymptotic theory and feasible inference for unbounded-kernel order-k U-statistics under clustered sampling and weakly dependent time-series. The analysis first builds the complete order-2 pipeline, moving from clustered data to exact $m$-dependence and then to near-epoch dependence. The same logic is subsequently extended to general order k greater or equal to 2. Under clustered sampling, the theory allows arbitrary within-cluster dependence and growing,…

二次型统计量的随机不变性检验及自相关应用AI 解读

Randomization testing with permutations is a very common nonparametric approach to hypothesis testing. However, randomization testing can be done with other group transformations including random rotations. In this work, we consider the problem of invariance in quadratic form statistics under a unified framework with closed form p-values. In particular, we propose a nonparametric variant of the classic Durbin-Watson test for testing for autocorrelation in time series data at…

利用时间压缩数据横截面估计长期关系AI 解读

Many empirical investigations of long-run relations are based on cross-section regressions in averaged or long differenced data that effectively have the time dimension of a $T\times N$ panel compressed. We analyze a class of time-compressed I(1) data and show that they have magnified variability stemming from the fact that the cross-section variance of a non-stationary panel `fans out' with time. Cross-section regressions in time compressed data can potentially yield…

隐藏结果的近端因果推断AI 解读

Methods that rely on proxies, without imposing strong parametric structure, are increasingly used to deal with unobserved variables in causal inference. One influential line of this work reconstructs latent distributions used to identify the target functional by exploiting eigenvalue eigenvector structure. Within this framework, we first establish identification of the full data law in the presence of hidden outcomes, and then develop influence function based estimators for…

存在干扰时网络实验的最优设计AI 解读

Experimental design under network interference is challenging because outcomes may depend on the treatment assignments of neighboring units. Existing approaches account for network structure but are typically assessed on small or simplified networks, limiting their applicability to complex real-world settings. We propose a network-aware treatment allocation framework that jointly accounts for allocation balance and network topology via an optimality criterion based on the…

有界异质性下的处理效应估计AI 解读

Specifications that impose constant treatment effects are common and can be biased under heterogeneity, whereas fully flexible alternatives can be imprecise or infeasible. Under a bound on the target-weighted variance of treatment effects, this paper proposes a generalized ridge estimator, $\texttt{regulaTE}$, that yields heterogeneity-aware confidence intervals (CIs). The ridge penalty is chosen to minimize the heterogeneity-aware CI length in a homoskedastic Gaussian…

部分识别离散响应模型的非参数贝叶斯推断AI 解读

This paper proposes a nonparametric Bayesian inference framework for partially identified discrete response models. The key observation is that these models map a reduced-form conditional choice probability to an identified set. Consequently, nonparametric Bayesian inference for the conditional probability mass function leads to Bayesian inference for the identified set. The inference framework nests conditional moment inequalities and linear systems with unknown…

双重差分中以研究范围换取可信度AI 解读

When parallel trends fails for some treated cohorts but not others, the average treatment effect on the treated (ATT), an average over all of them, is exactly the target that becomes hard to recover. We propose changing the estimand rather than defending it. The credible-subpopulation local ATT (LATT) is the effect for the subpopulation of cohorts whose parallel trends is credible, and it is point-identified under parallel trends for the selected cohorts alone, a weaker…

函数型时间序列的频域自助法AI 解读

A frequency domain bootstrap procedure for functional time series is proposed and applied to the class of spectral mean operators. The procedure works by first generating independent pseudo periodogram operators across the positive Fourier frequencies using an estimator of the spectral density operator involved. Functional replicates of the spectral mean operators of interest are then generated. Through an additive, projection-based decomposition of the bootstrapped spectral…