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存在干扰时合成控制的稳健回归方法AI 解读

Synthetic control methods are widely used for policy evaluation, but most existing approaches rule out interference among units, compromising validity when such effects are present. We develop a framework that accommodates contaminated donor pools and unknown interference patterns through two stages: factor-model adjustment for unobserved confounding, followed by robust regression in which direct and interference effects appear as a sparse outlier component. We study two…

交叉试验的统计方法：经典与近期进展综述AI 解读

A comprehensive review of the literature on crossover design is needed to highlight its evolution, applications, and methodological advancements across various fields. Given its widespread use in clinical trials and other research domains, understanding this design's challenges, assumptions, and innovations is essential for optimizing its implementation and ensuring accurate, unbiased results. This article extensively reviews the history and statistical inference methods for…

Wearable devices for continuous electronic health monitoring often capture data at frequent intervals under a dense functional design. The focal point is the analysis of longitudinal functional data, wherein functional trajectories are observed repeatedly over time. This work is motivated by the interest in assessing the efficacy of a noninflammatory medication, meloxicam, on the daily activity levels of household cats with a pre-existing condition of osteoarthritis under a…

We present a decision-theoretic justification for viewing the question of how to best choose where to experiment in order to optimize external validity as a $k$-median problem, a popular problem in computer science and operations research. In particular, when treatment effect heterogeneity across experimental and policy-relevant sites is substantial (in a sense we make precise), we present conditions under which minimizing the worst-case, welfare-based regret among all…

随机正规算子近似下Landweber迭代的不确定性量化AI 解读

Iterative methods are extremely popular for solving linear ill-posed inverse problems. While deterministic convergence, or more precisely, semi-convergence of such methods is widely studied, the problem of quantifying uncertainty in the resulting reconstructions due to random noise in the observed data has received far less attention. In this work, we study uncertainty quantification for the Landweber iteration in linear inverse problems using the Radon transform as a…

函数型解释变量含测量误差的标量对函数回归AI 解读

We consider the problem of scalar-on-function regression. Most existing methods implicitly assume that the functional covariates are observed exactly, but in practice, they are often contaminated by measurement error. Our goal, therefore, is to deal with the problem of scalar-on-function regression when the regressor functions are observed with error. In this paper, we propose a simulation-extrapolation method to correct for the attenuation of estimated coefficient functions…

Large language models are increasingly used to classify text at scale. Because their outputs are stochastic, it is common to query a model several times per item and aggregate the results, and latent class models offer a principled way to convert such repeated ratings into estimates of outcome prevalence and of the model's error rates. These models assume that repeated ratings of the same item are conditionally independent given the true label. We examine this assumption…

异方差噪声下高维时间序列的因子模型估计AI 解读

Factor modeling provides a framework for extracting common components from high-dimensional time series. We develop an estimator of the factor loading space that combines contemporaneous covariance with lagged autocovariance information. Under cross-sectionally heteroskedastic white noise, the contemporaneous covariance contains heterogeneous diagonal noise contributions. We apply the existing HeteroPCA algorithm to the combined estimation matrix to correct this…

张量因子模型中多个变点的检测与模态识别AI 解读

We study the problem of modeling high-dimensional tensor-valued time series under a Tucker decomposition-based factor model while permitting the presence of multiple structural change points. Such data call for a method that (i) handles high dimensionality and complex dependence while achieving consistency in multiple change points and further, (ii) addresses a unique challenge posed by the multi-dimensional array setting, of identifying the modes associated with each change…

关于 Forré 条件独立概念与连续变量因果演算的札记AI 解读

Recently, Forré (arXiv:2104.11547, 2021) introduced transitional conditional independence, a notion of conditional independence that provides a unified framework for both random and non-stochastic variables. The original paper establishes a strong global Markov property connecting transitional conditional independencies with suitable graphical separation criteria for directed mixed graphs with input nodes (iDMGs), together with a version of causal calculus for iDMGs in a…

扰动效用模型的 Fenchel–Young 估计量AI 解读

Perturbed Utility Models (PUMs) represent discrete choice as utility maximization under convex perturbation or regularization, encompassing a broad class of choice models, including Multinomial Logit (MNL) and models that permit zero choice probabilities. Because, for general perturbations, maximum likelihood may be non-convex, and the negative log-likelihood is infinite when a sparse model assigns zero probability to an observed choice, we specialize the Fenchel--Young…

基础模型还是经典模型？TimesFM-3 与经典时间序列模型的模拟比较AI 解读

Time-series foundation models such as Google's TimesFM-3 forecast series they were never trained on, but whether they should replace classical methods such as ARIMA and exponential smoothing is hard to settle on public benchmarks, because few benchmark datasets are absent from every model's pre-training corpus. This exploratory study compares TimesFM-3 with classical methods on data generated from nine known processes, which the model cannot have seen. Across 7200 series and…

商品风险的构成：微观、市场与全经济来源AI 解读

We study the anatomy of commodity risk by distinguishing micro, market-level, and economy-wide sources. We develop a two-stage "divide-and-conquer" framework that allows sensitivities to these risk sources to vary across commodities while treating economy-wide risk as latent. The first stage uses defactored instrumental-variable estimation to recover commodity-specific sensitivities to micro and market conditions. The second combines principal components with…

熵正则化最优传输下的部分识别AI 解读

In many statistical settings, the available data and maintained assumptions do not suffice to uniquely identify the model parameters of interest. In such cases, one can only identify sets which are guaranteed to contain the true parameters. These are often characterized through linear programs that optimize over models compatible with the observed data. These programs can be infinite-dimensional in the optimizer and the number of constraints. We provide a unified way to…

谱正则化下均值与协方差函数的极小极大最优估计AI 解读

Estimation of the mean and covariance functions is a fundamental problem in functional data analysis, particularly for discretely observed functional data. In this work, we study a regularization-based framework for estimating the mean and the covariance functions within a reproducing kernel Hilbert space (RKHS) setting. Our approach utilizes a spectral regularization technique under Hölder-type source conditions, allowing for a broad class of regularization schemes and…

挖掘因果关系：人工智能辅助搜索工具变量AI 解读

The instrumental variables (IVs) method is a leading empirical strategy for causal inference. Finding IVs is a heuristic and creative process, and justifying their validity---especially exclusion restrictions---is largely rhetorical. We propose using large language models (LLMs) to search for new IVs through narratives and counterfactual reasoning, similar to how a human researcher would. The stark difference, however, is that LLMs can dramatically accelerate this process…

不可测混杂下因果效应估计的近端平衡方法AI 解读

Estimating causal effects from observational data is central to science and policy, but the effects are not identified when confounders are unmeasured. Proximal causal inference addresses this problem with proxies of the unmeasured confounders. However, existing proxy-based approaches either designate proxy roles and solve an inverse problem, which is ill-posed and hard to estimate with high-dimensional proxies, or use a latent-variable model, which assumes that the learned…

多输出系统辨识的有限样本界AI 解读

This paper presents uniform-in-time error bounds for regularized linear regression with vector-valued outputs and conditionally zero-mean subgaussian noise. By revisitileveraging self-normalized martingale arguments, we obtain bounds that apply directly to multi-output regression. These bounds are tighter and hold in more general settings than commonly used methods in finite-sample system identification. Furthermore, because of a novel treatment of the regularization bias,…

受约束分类与政策学习AI 解读

Modern machine learning approaches to classification, including AdaBoost, support vector machines, and deep neural networks, utilize surrogate loss techniques to circumvent the computational complexity of minimizing empirical classification risk. These techniques are also useful for causal policy learning problems, since estimation of individualized treatment rules can be cast as a weighted (cost-sensitive) classification problem. Consistency of the surrogate loss approaches…

离散选择面板模型中平均效应的界AI 解读

In discrete choice panel data, estimation of average effects is crucial for quantifying the effect of covariates, and for policy evaluation and counterfactual analysis. However, in short panels with individual-specific effects, challenges arise due to partial identification and the incidental parameter problem. In particular, estimating the sharp identified set of average effects becomes impractical when covariates have large support sets, such as when they are continuous.…

含时间趋势试验中的估计目标与估计方法AI 解读

In platform trials, model-based approaches typically estimate treatment effects conditional on calendar time. However, scientific and regulatory interest often lies in treatment effects defined for a target population spanning multiple enrollment periods, requiring explicit consideration of how effects should be averaged across time. This raises two fundamental challenges. The first is the definition of the appropriate target estimand when combining data across multiple…

随机实验的一般设计型框架与估计器AI 解读

We describe a widely applicable design-based framework for drawing causal inference in randomized experiments. Causal effects are defined as linear functionals evaluated at unit-level potential outcome functions. Assumptions about the potential outcome functions are encoded as function spaces. This makes the framework expressive, allowing experimenters to formulate and investigate a wide range of causal questions that previously could not be investigated with design-based…

WEIRDO：以弱残差正则化估计 Doob h 变换的扩散引导AI 解读

We study the problem of estimating the guidance that steers the distribution learned by a diffusion generative model toward a tilted target $q_0 \propto w\,p_0$ at inference time. Relying on the stochastic optimal control approach, we observe that the exact drift correction is the gradient of the logarithm of Doob's $h$-function, and we study the problem of estimating it from a sample. In the present paper, we assume that the score of the pretrained model is available, that…

高维非线性因子模型的最优估计AI 解读

This paper studies optimal estimation of large-dimensional nonlinear factor models. The key challenge is that the observed variables are possibly nonlinear functions of some latent variables, with the functional forms left unspecified. A local principal component analysis method combining K-nearest neighbors matching and principal component analysis is proposed to estimate the factor structure and recover information on latent variables and latent functions. Large-sample…