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网络干扰下分位数效应的一致推断AI 解读

This paper studies quantile treatment and spillover effects in network experiments. Average spillover effects reveal how treating a unit's neighbors affects its outcome on average, but mask the heterogeneity of these effects across the outcome distribution. We define structural quantile effects that compare outcome quantiles between exposure states, characterizing how own treatment and exposure to treated neighbors affect different parts of the outcome distribution. Building…

含噪网络测量下时变网络构成与预测依赖的分离AI 解读

A common question about networked time series is whether outcomes changed because shocks transmit more strongly or because the pattern of connections changed. Standard practice inserts a recorded network into an outcome regression and reads movements of the fitted coefficient as changes in transmission strength. When the network is latent, time varying, and measured with error, this reading fails: changes in strength and changes in composition can produce the same outcome…

显示性社会网络AI 解读

The linear-in-means model is the standard empirical model of peer effects and asks that an agent's choice or outcome is a combination of their ideal point and the mean outcome of their group. Using choice data and exogenous group variation, we develop a revealed preference style test for the linear-in-means model. This test is formulated as a linear program and can be interpreted as a condition about differentiating the behavior of each agent in a consistent manner. We then…

多重处理因果推断中的重心融合 Gromov–Wasserstein 平衡AI 解读

Estimating heterogeneous single and interaction treatment effects from observational data under multiple simultaneous treatments is crucial for decision-making. To mitigate estimation variance, previous studies balance representation distributions between every pair of treatment patterns. However, such pairwise balancing scales quadratically with the number of treatment patterns and fails to preserve consistent local proximity structures across patterns, which degrades…

交互与高维约束下的一致有效推断AI 解读

Asymptotic normality approximations often fail to hold for extremum estimators when the true value of the parameter is at or close to the boundary of a parameter space. I analyze and develop tests using a quasi-unconstrained estimator, which is asymptotically normal even when the true parameter vector is near or at the boundary. These results generalize previous work with this estimator by allowing for more types of constraints and showing how the method can naturally be…

利用阳性组复检的群组检测评估处理效应AI 解读

Group testing is an established, highly cost-effective strategy for population-level disease surveillance that identifies positive individuals by pooling biological specimens. Originally introduced during World War II for large-scale screening and heavily utilized in modern high-throughput public health infrastructure, traditional group testing methods are restricted to purely associational analyses. Consequently, they lack the capacity to infer the direct causal effect of…

空间函数对函数分位数回归AI 解读

This paper introduces a novel penalized spatial function-on-function quantile regression framework for analyzing spatially indexed functional data, bridging a critical gap between spatial functional models and quantile regression. Our work makes three key contributions. First, we propose the first spatial function-on-function quantile regression model that jointly accounts for spatial correlation across curves through a functional spatial autoregressive structure while…

用于神经影像数据的贝叶斯张量回归AI 解读

Multidimensional array data, or tensors, arise naturally in neuroimaging and other high-dimensional applications. We propose a parsimonious Bayesian tensor regression model for studies in which a brain image is the response and predictors are vector-valued covariates. The method extends Bayesian envelope dimension reduction to tensor responses, identifying material subspaces that contain regression information while removing variation that is immaterial to the predictors.…

靶向深度生存对比：神经网络下处理特定生存收益的有效推断AI 解读

Neural survival models are increasingly asked to support counterfactual claims---how much a treatment would change survival in a population---rather than only prognostic risk scores. Answering such questions from observational data requires valid inference for treatment-specific survival contrasts under confounding and covariate-dependent censoring, targets for which standard deep survival estimators are biased and provide no honest uncertainty. We propose Targeted Deep…

无序对空间上的协方差核：理论与网络数据应用AI 解读

Many scientific problems are relational: the quantity of interest is a connection between two objects, while information about similarity is available for the objects themselves. We develop a covariance framework for unordered relationships that transfers object-level geometry to the relations they form while preserving endpoint identity and invariance to ordering. Building on symmetric pairwise-kernel representations, we develop theory for the loop-free domains used in…

以空间相关超参数连接地区高斯过程的区域时间序列模型AI 解读

In many applied settings, areal data are observed repeatedly over long time periods, as commonly occurs in infectious disease surveillance and environmental or demographic monitoring. Accurate characterization of local temporal dynamics and uncertainty is important for monitoring disease trends, identifying local changes, and supporting public health decision-making. Traditional spatio-temporal models generally represent spatial, temporal, and space-time interaction…

多方法因果证据综合：以跨方法趋同证据排序候选驱动因素AI 解读

Practitioners inferring causality from observational data usually rely on a single method and treat its output as causal truth. Recent tools select an optimal method for a dataset, and recent ensembles aggregate multiple causal-discovery algorithms into one graph, but little work pools evidence across different mathematical traditions, including non-causal ones. We present Multi-Method Causal Evidence Synthesis (MCES), a framework that ranks which candidate drivers in an…

多元时空回归：惩罚模型选择与实证应用AI 解读

This paper develops the statistical foundations of a multivariate general nesting spatio-temporal (MGNST) regression framework for analyzing spatial, temporal, and cross-equation dependence among responses. Four parameter matrices represent spatial lag dependence, spatial error dependence,temporal autoregression, and contemporaneous error covariance. Their off-diagonal elements allow dependence to propagate within and across responses. Matrix restrictions yield eleven…

存在溢出效应的实验与准实验：基于设计的检验和推断AI 解读

Economic policies rarely affect only their direct targets. To study these spillovers, researchers summarize who else was treated with a simple exposure measure, such as the share of treated neighbors within a radius. But for many settings, economic theory provides little guidance on choosing the functional form (e.g., ring) of that measure or its parameters (e.g., radius). We show that the data can inform both choices. Correctly specified exposure measures imply…

用于成分回归的图自适应马蹄先验AI 解读

Compositional predictors, such as microbiome abundances, pose unique challenges in variable selection due to their unit-sum constraint and inherent dependencies. Existing approaches often rely on fixed association graphs derived from phylogenetic or ecological distances, which may not reflect outcome-relevant relationships. We propose GRACE (GRaph-Adaptive horseshoe for Compositional rEgression), a fully Bayesian framework that enforces compositional constraints, performs…

调查抽样与因果推断中的最优控制变量AI 解读

We propose a family of control variate estimators for variance reduction in design-based survey sampling and causal inference, with and without interference. In these settings, inverse probability weighting (IPW) estimators are widely used, but may have large variance when sampling, treatment, or exposure probabilities are small. Building on the observation that several common estimators, including the Hajek estimator, the normalized estimator, the augmented inverse…

学习宏观经济语言AI 解读

We show how state-of-the-art large language models (LLMs) can be trained effectively on limited historical data for macroeconomic forecasting. We estimate a dynamic stochastic general equilibrium (DSGE) model with stochastic volatility and Student-t shocks on an initial segment of the data to obtain a posterior distribution over structural parameters. We sample from this posterior to generate millions of theory-consistent synthetic panels that, when mixed with macroeconomic…

似然模型偶然参数问题的 Neyman 正交化方法AI 解读

A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization allows the estimator of the nuisance parameters to converge at a slower-than-parametric rate. It may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading…

条件随机分组下同伴效应的估计与推断AI 解读

Empirical studies of peer effects often exploit conditional random assignment to peer groups within urns. We develop a GMM framework for estimation and inference in this setting. The framework separately identifies endogenous and contextual peer effects and nests tests of random peer-group assignment as a special case. It permits unknown heteroskedasticity and corrects finite-urn bias in variance estimation. Its asymptotic theory allows the number of peer groups to grow…

部分干扰下用于倾向得分估计的混合效应结果自适应 LassoAI 解读

Interference occurs when one individual's treatment or exposure affects another individual's outcome. In particular, we assume partial interference, where individuals are divided into groups such that there is no interference between individuals in different groups. In observational studies, inverse probability weighting (IPW) based on propensity scores is often used for causal effect estimation. However, under partial interference, the group-level propensity score must be…

高维矩阵值时间序列的时空自回归AI 解读

Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals across multiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions…

带序列相关扰动的非线性回归神经网络：来自云量的证据AI 解读

We propose a new treatment of nonlinear regression with serially correlated disturbances that incorporates autoregressive moving average structures into feedforward neural networks. The resulting model provides an alternative to modeling temporal dependence using lagged variables. In simulations, the proposed method accurately recovers regression functions of varying complexity and the underlying error dynamics across a range of time-series lengths and signal-to-noise…

网络上的多尺度动态依赖估计AI 解读

In many settings, observed multivariate time series are often nonstationary in nature, i.e., their second order properties vary over time. An additional feature is that their cross-channel dependencies are structured by an underlying network. Together, they give rise to complex interactions between temporal dynamics and network topology. We propose Locally Stationary Wavelet processes on Networks (Net-LSW), a new framework for modelling multiscale, time-varying dependencies…

空间场景下设计型推断中的结果建模AI 解读

In the face of spatial interference, researchers are often interested in estimating treatment effects at specific points located in space. Wang et al. (2025) and Pollmann (2023) provide design-based frameworks for estimating spillover effects on points located across a range of distances from interventions. Although these frameworks are design-based, we show their proposed estimands rely on outcomes that are directly unobservable and therefore, require outcome modeling. When…