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高维协变量辅助的排序数据建模AI 解读

Ranking data in modern applications often involves covariate vectors whose dimension grows with the number of objects. Identifying relevant covariates and integrating them with item-specific utilities can yield better predictive performance. In this work, we study a Plackett--Luce model that incorporates both item-specific utilities and high-dimensional covariate effects. Under a sparsity assumption on the covariate coefficients, we establish a necessary and sufficient…

纵向结局与替代标志物联合评价的因果状态空间框架AI 解读

Surrogate markers offer the potential to reduce the burden of data collection by replacing costly or invasive primary outcomes with more accessible measurements, provided that they can faithfully indicate the effectiveness of a treatment. However, appropriate evaluation of a surrogate is particularly complex in longitudinal studies, where both outcomes and surrogates can evolve dynamically over time and interest lies not only in the treatment effect at one time, but rather…

Dirichlet-Tree 先验用于贝叶斯因果森林AI 解读

Bayesian Causal Forests (BCF) estimate heterogeneous treatment effects with a separate ensemble of treatment trees. The prior on the leaf values of the treatment trees determines whether sampling variability is attributed to the treatment effect. Independent shrinkage priors exert no control over how a tree allocates variability across its leaves, and their total prior variance grows as leaves are added. We propose the Dirichlet-Tree Bayesian Causal Forest, which jointly…

Granular Instrumental Variables (GIV), introduced by Gabaix and Koijen (2024), use idiosyncratic shocks to large units as instruments for aggregate endogenous variables. I develop asymptotic theory for GIV in large panels where both the cross section and time series grow (\(N,T\to\infty\)). I make two main contributions. First, I show that estimating the individual shocks used to construct the granular instrument has a first-order effect on the asymptotic variance of the GIV…

In the copula-based approach to univariate time series modeling, the finite-dimensional temporal dependence of a stationary time series is captured by a copula. Recent studies investigate how copula-based time series models can be generalized to have long-term autoregressive effects. We study a generalization that comes from a Markov sequence of order p and a q-dependent sequence. We derive the relation of the model to Gaussian-ARMA models and to the Gaussian-GARCH(1,1)…

Difference-in-differences (DID) approaches estimate causal effects with observational data before and after an intervention. DID traditionally estimates the average treatment effect among the treated and assumes parallel trends in outcome means. With skewed outcomes, a transformation is often needed to compare means; however, transformations may be difficult to choose and results may be sensitive to the choice and must be interpreted on the transformed scale. Recent DID…

合成控制法：精确几何表述AI 解读

The synthetic control method matches a treated unit to a convex combination of donor units, with the relative importance of each predictor determined by a weight matrix $V$. In standard practice, $V$ is selected through a bi-level optimization problem that is nonconvex and lacks convergence guarantees. This article derives an exact geometric solution for synthetic control estimators. For any positive definite $V$, a synthetic control is given by the projection of the treated…

持续型面板局部投影的近似均匀偏差校正AI 解读

Panel local projections are widely used to estimate impulse responses in policy analysis. This paper examines the properties of the fixed-effect estimator and various bias-correction methods in panel local projections, focusing on settings with autoregressive control variables. We analyze the bias and variance of these estimators while allowing for persistence in both the target and control variables. Our theoretical findings indicate that the half-panel jackknife has…

Deep-π*：基于神经网络的趋势通胀估计AI 解读

Most empirical models of trend inflation treat it as a latent random walk driven by unobserved shocks, so they cannot say which economic conditions move it. Deep-$π^*$ makes the innovations to the trend a possibly nonlinear function of a large number of macroeconomic indicators, estimated with neural networks, so that Shapley values can be used to attribute every movement of the trend to the indicators behind it. Two complementary loss functions define a current trend, which…

兼顾单元格与个案异常的稳健多元回归及推断AI 解读

Multivariate linear regression is a fundamental statistical task, but classical estimators such as ordinary least squares are highly sensitive to outliers. These may occur as casewise outliers that affect entire observations, or as outlying cells, that are individual contaminated entries in the predictor and/or response matrix. Moreover, modern datasets frequently contain missing values and are high-dimensional. To address these challenges we propose the cellwise…

利用历史数据与自助推断的协变量自适应随机化AI 解读

Covariate-adaptive randomization (CAR) can improve the efficiency of average treatment effect (ATE) estimation by reducing chance imbalances in prognostic baseline covariates. A recently proposed CAR method that balances multiple covariates, including continuous ones, can achieve optimal precision when the conditional mean of the outcome can be expressed as a linear combination of the covariate features used for the CAR procedure. However, this condition may be difficult to…

合成环境下的因果稳健性AI 解读

We study robust prediction when the training data come from a single observational distribution. Causality-oriented methods for robust prediction usually use heterogeneity across observed environments both to learn an invariant prediction function and to determine the perturbations against which it is robust. We show that both can be recovered from one distribution under a structural additive-noise mechanism for the response. Under the structural model considered here, the…

一般随机块模型中谱初始化变分 EM 的最优社区恢复AI 解读

We prove a Chernoff-exponent guarantee for the output of spectrally initialized batch variational EM after a prescribed iteration budget, without assuming global maximization of its variational objective. The iteration repeatedly estimates the entire block probability matrix and community proportions from the same sparse graph. We consider a fixed number of communities with proportions bounded away from zero and fixed, positive, distinct connectivity profiles; neither…

Instrumental variable (IV) methods are widely used to estimate the price elasticity of demand from observational data. However, naive IV estimators may be inconsistent in settings with autocorrelated time series. We argue that causal graphs taking into account time structure can simplify IV identification and help select consistent estimators. More precisely, we propose to model equilibrium conditions using causal mechanism time graphs. These (acyclic) graphs allow us to…

基于期望分位过程的免调参处理效应异质性检验AI 解读

This paper develops tuning-free tests for treatment-effect heterogeneity. By characterizing the null hypothesis as constancy of the expectile treatment-effect curve, we construct test statistics as continuous functionals of the mean-centered sample expectile process. We establish a uniform Bahadur representation, derive the asymptotic properties of the test statistics under the null and fixed alternatives, and show nontrivial power against local alternatives approaching the…

非高斯图离散Lyapunov模型的结构可识别性AI 解读

This paper studies the problem of structural identifiability for graphical discrete Lyapunov models in the non-Gaussian setting from observational data. A graphical discrete Lyapunov model associated with a graph $G$ consists of the set of stationary distributions of stable first-order vector autoregressive models whose transition matrices have sparsity patterns determined by $G$. This model has a causal interpretation as $G$ can be viewed as encoding dynamic causal…

复杂调查加权双重得分匹配的总体因果效应估计AI 解读

Double score matching is an emerging method for causal inference that combines propensity and prognostic scores to achieve dimension reduction and model robustness. However, when the observational data are drawn from a complex survey, the existing literature does not provide sufficient guidance on the proper use of survey weights in matching strategies for population-level causal inference. We develop weighted double score matching (WDSM) that provides a unified framework…

多臂随机对照试验的 LATE 目标实验设计AI 解读

We study the design of multi-arm experiments that identify local average treatment effects under imperfect compliance. With three or more arms, conventional randomization fails to identify margin-specific effects even under strong extensions of monotone compliance. A choice-theoretic analysis unifies these extensions and motivates a two-stage design: subjects report a preferred arm and are randomized conditional on it. If treatment is confined to the assigned or reported…

聚类结果线性模型的随时有效推断：精确多元检验与聚类稳健置信序列AI 解读

We extend anytime-valid inference for linear models from independent scalar outcomes to units that contribute a vector of dependent outcomes. We develop a parametric and a nonparametric treatment. For the Gaussian multivariate linear model with an unstructured error covariance, we derive a closed-form Bayes factor under the right-Haar prior and a matrix-normal mixture alternative and prove that it is an exact e-value at every sample size and a test martingale in the…

数据草图与堆叠：高维特征高斯过程回归预测推断的两种策略融合AI 解读

This article focuses on drawing computationally-efficient predictive inference from Gaussian process (GP) regressions with a large number of features when the response is conditionally independent of the features given the projection to a noisy low dimensional manifold. Bayesian estimation of the regression relationship using Markov Chain Monte Carlo and subsequent predictive inference is computationally prohibitive and may lead to inferential inaccuracies since accurate…

含共同未观测异质性的线性回归AI 解读

We study inference on common slope parameters in linear panel models with two-way unobserved heterogeneity. In linear regression, bias arises from unobserved heterogeneity that is shared between the residual and the covariates. Our common unobserved heterogeneity (CUH) estimator targets this common variation directly: it searches over functionals of the residualised outcome for variation that also resides in the covariates, and seeks to further project out only bias-inducing…

度量空间时间序列的在线分布变化检测AI 解读

We propose an online testing framework for detecting distributional changes in serially dependent data with values in a separable metric space. Based on two-sample $U$-statistics, the framework encompasses sequential analogs of energy distance and maximum mean discrepancy (MMD) procedures while accommodating temporal dependence. We establish asymptotic theory for finite and open-ended monitoring horizons that characterizes the full asymptotic run-length distribution under $H_0$…

基于条件扩散模型的限价订单簿概率预测AI 解读

Modeling limit order book (LOB) dynamics is central to market forecasting and execution-strategy evaluation. While diffusion models achieve strong empirical performance, existing LOB snapshot models typically lack a learned recurrent encoder and formal statistical guarantees. We introduce LOBGrad, a conditional DDPM framework for probabilistic LOB forecasting that combines an autoregressive RNN encoder with a diffusion-based generator. In particular, we establish an…

基于可行性检验的线性规划推断AI 解读

This paper studies inference on the value of a linear program (LP) when the objective and constraints are estimated. Using LP duality, we construct one- and two-sided confidence intervals by inverting tests of the feasibility of systems of linear inequalities. Existing tests yield asymptotically valid confidence intervals but can have poor power. We propose a modification that augments these systems with estimated ``box constraints.'' The resulting procedure has power…