每日简报

多参数广义耙估计的最优两阶段抽样设计AI 解读

Large observational datasets, including those derived from electronic health records, are a valuable resource for medical research but are often affected by missingness, measurement error, and misclassification. Two-phase sampling with generalized raking (GR) estimation is an efficient and robust approach to statistical inference in such settings. In this approach, variables that are unavailable or measured with error in a large phase 1 cohort are obtained with…

用于因果效应不确定性量化的贝叶斯目标极大似然估计AI 解读

Robust decision making involves making decisions in the presence of uncertainty and is often used in critical domains such as healthcare, supply chains, and finance. Causality plays a crucial role in decision-making as it predicts the change in an outcome (usually a key performance indicator) due to a treatment (also called an intervention). To facilitate robust decision making using causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum…

离散值时间序列的预测推断AI 解读

For discrete-valued time series, predictive inference cannot be implemented through the construction of prediction intervals to some predetermined coverage level, as this is the case for real-valued time series. To address this problem, we propose to reverse the construction principle by considering preselected sets of interest and estimating the probability that a future observation of the process falls into these sets. The accuracy of the prediction is then evaluated by…

因子增强框架下的二元响应预测AI 解读

In this paper, we propose a novel factor-augmented forecasting regression model with a binary response variable. We develop a maximum likelihood estimation method for the regression parameters and establish the asymptotic properties of the resulting estimators. Monte Carlo simulation results show that the proposed estimation method performs very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through an application to U.S. recession…

贝叶斯因果发现：后验集中与最优检测AI 解读

We consider the problem of Bayesian causal discovery for the standard model of linear structural equations with equivariant Gaussian noise. A uniform prior is placed on the space of directed acyclic graphs (DAGs) over a fixed set of variables and, given the graph, independent Gaussian priors are placed on the associated linear coefficients of pairwise interactions. We show that the rate at which the posterior on model space concentrates on the true underlying DAG depends…

具有有序结果变量的成对数据模型AI 解读

We consider ordered logit models for directed network data that allow for flexible sender and receiver fixed effects that can vary arbitrarily across outcome categories. This structure poses a significant incidental parameter problem, particularly challenging under network sparsity or when some outcome categories are rare. We develop the first estimation method for this setting by extending tetrad-differencing conditional maximum likelihood (CML) techniques from binary…

兼得有限样本有效与一致渐近精确：期望和线性回归的置信区间AI 解读

We contribute to bridging the gap between large- and finite-sample inference by studying confidence sets (CSs) that are both non-asymptotically valid and asymptotically exact uniformly (NAVAE) over semi-parametric statistical models. NAVAE CSs are not easily obtained; for instance, we show they do not exist over the set of Bernoulli distributions. We first derive a generic sufficient condition: NAVAE CSs are available as soon as uniform asymptotically exact CSs are. Second,…

利用注意力语言模型解读消费者偏好AI 解读

This paper proposes a new demand estimation method using attention-based language models. An encoder-only language model is trained in a two-stage process to analyze the natural language descriptions of used cars from a large US-based online auction marketplace. The approach enables semi-nonparametrically estimation for the demand primitives of a structural model representing the private valuations and market size for each vehicle listing. In the first stage, the language…

熵与复杂度的联合渐近分布AI 解读

We derive the asymptotic distribution of ordinal-pattern frequencies under weak dependence conditions and investigate the long-run covariance matrix not only analytically for moving-average, Gaussian, and the novel generalized coin-tossing processes, but also approximately by a simulation-based approach. Then, we deduce the asymptotic distribution of the entropy-complexity pair, which emerged as a popular tool for summarizing the time-series dynamics. Here, we make the…

任意结构潜变量下线性因果模型的 Trek 路径参数识别AI 解读

We develop a criterion to certify whether causal effects are identifiable in linear structural equation models with latent variables. Linear structural equation models correspond to directed graphs whose nodes represent the random variables of interest and whose edges are weighted with linear coefficients that correspond to direct causal effects. In contrast to previous identification methods, we do not restrict ourselves to settings where the latent variables constitute…

面板数据分位数回归的分区野生自助法AI 解读

Practical inference procedures for quantile regression models of panel data have been a pervasive concern in empirical work, and can be especially challenging when the panel is observed over many time periods and temporal dependence needs to be taken into account. In this paper, we propose a new bootstrap method that applies random weighting to a partition of the data -- partition-invariant weights are used in the bootstrap data generating process -- to conduct statistical…

分批自适应网络形成AI 解读

Networks are central to many economic and organizational applications, including workplace team formation, social platform recommendations, and classroom friendship development. In these settings, networks are modeled as graphs, with agents as nodes, agent pairs as edges, and edge weights capturing pairwise production or interaction outcomes. This paper develops an adaptive, or \textit{online}, policy that learns to form increasingly effective networks as data accumulates…

技能形成模型的灵活估计AI 解读

This paper examines estimation of skill formation models, a critical component in understanding human capital development and its effects on individual outcomes. Existing estimators are either based on moment conditions and only applicable in specific settings or rely on distributional approximations that often do not align with the model. Our method employs an iterative likelihood-based procedure, which flexibly estimates latent variable distributions and recursively…

加权推断：不同估计目标与重抽样机制下的残差化区间AI 解读

Weighting procedures are used in observational causal inference to adjust for covariate imbalance within the sample. Common practice for inference is to estimate robust standard errors from a weighted regression of outcome on treatment. However, it is well known that weighting can inflate variance estimates, sometimes significantly, leading to standard errors and confidence intervals that are overly conservative. We instead examine and recommend the use of robust standard…

圆周数据的因果推断AI 解读

In causal inference, a fundamental task is to estimate the effect resulting from a specific treatment, which is often handled with inverse probability weighting. Despite an abundance of attention to the advancement of this task, most articles have focused on linear data rather than circular data, which are measured in angles. In this article, we extend the causal inference framework to accommodate circular data. Specifically, two new treatment effects, average direction…

乘法工具变量模型下非线性反事实泛函的推断AI 解读

Instrumental variable (IV) methods play a central role in causal inference, particularly in settings where treatment assignment is confounded by unobserved variables. IV methods have been extensively developed in recent years and applied across diverse domains, from economics to epidemiology. In this work, we study the recently introduced multiplicative IV (MIV) model and demonstrate its utility for causal inference beyond the average treatment effect. In particular, we show…

马尔可夫结构因果模型的规范表示：反事实推理框架AI 解读

Counterfactual reasoning aims at answering contrary-to-fact questions like ``Would have Alice recovered had she taken aspirin?'' and corresponds to the most fine-grained layer of causation. Critically, while many counterfactual statements cannot be falsified-even by randomized experiments-they underpin fundamental concepts like individual-wise fairness. Therefore, providing models to formalize and implement counterfactual beliefs remains a fundamental scientific problem. In…

生存函数的因果推断AI 解读

In this expository paper, we consider the problem of causal inference and efficient estimation for the counterfactual survivor function. This problem has previously been considered in the literature in several papers, each relying on the imposition of conditions meant to identify the desired estimand from the observed data. These conditions, generally referred to as either implying or satisfying coarsening at random, are inconsistently imposed across this literature and, in…

交互、分组与非可分固定效应：新面板计量方法的实践指南AI 解读

The past 20 years have brought fundamental advances in modeling unobserved heterogeneity in panel data. Interactive Fixed Effects (IFE) proved to be a foundational framework, generalizing the standard one-way and two-way fixed effects models by allowing the unit-specific unobserved heterogeneity to be interacted with unobserved time-varying common factors, allowing for more general forms of omitted variables. The IFE framework laid the theoretical foundations for other forms…

污染下高维双重稳健因果推断的惩罚经验似然AI 解读

We propose a doubly robust estimator for the average treatment effect in high dimensional low sample size observational studies, where contamination and model misspecification pose serious inferential challenges. The estimator combines bounded influence estimating equations for outcome modeling with covariate balancing propensity scores for treatment assignment, embedded within a penalized empirical likelihood framework using nonconvex regularization. It satisfies the oracle…

面板数据模型中辅助性—充分性交织 MCMC 的收敛速率AI 解读

Improving Markov chain Monte Carlo algorithm efficiency is essential for enhancing computational speed and inferential accuracy in Bayesian analysis. These improvements can be effectively achieved using the ancillarity-sufficiency interweaving strategy (ASIS), an effective means of achieving such gains. Herein, we provide the first rigorous theoretical justification for applying ASIS in Bayesian hierarchical panel data models. Asymptotic analysis demonstrated that when the…

混合对照试验中偏差界定的非参数敏感性分析AI 解读

We study hybrid control trials (HCTs), in which a randomized controlled trial (RCT) is augmented with external control patients. Existing approaches for HCTs typically assume conditional exchangeability of the concurrent and external controls to identify trial-specific effects. When violated, this can induce substantial unquantified bias, which in turn limits the acceptability of HCTs in regulatory settings. We treat violations of mean exchangeability as omitted variable…

函数型周期ARMA过程AI 解读

Periodicity is a common feature of time series. For finite-dimensional data, periodic autoregressive moving average (ARMA) models have been extensively studied. In functional time series analysis, AR models have been extended to incorporate periodicity, but existing approaches remain incomplete and do not cover the ARMA setting. This paper develops a rigorous theoretical framework for functional periodic ARMA (fPARMA) processes in general separable Hilbert spaces. The…

弱因子究竟有多弱？信号加噪声模型中信号强度的统一推断AI 解读

The paper analyzes four classical signal-plus-noise models: the factor model, spiked sample covariance matrices, the sum of a Wigner matrix and a low-rank perturbation, and canonical correlation analysis with low-rank dependencies. The objective is to construct confidence intervals for the signal strength that are uniformly valid across all regimes - strong, weak, and critical signals. We demonstrate that traditional Gaussian approximations fail in the critical regime.…