论文检索

加权估计量的方差敏感性分析与更有信息的界限AI 解读

Abstract Weighting methods are popular tools for estimating causal effects, and assessing their robustness under unobserved confounding is important in practice. Current approaches to sensitivity analyses rely on bounding a worst-case error from omitting a confounder. In this paper, we introduce a new sensitivity model called the variance-based sensitivity model, which instead bounds the distributional differences that arise in the weights from omitting a confounder. The…

概率与统计史研究之五十一：最早的条件 logistic 回归AI 解读

Summary Statisticians and epidemiologists generally cite the publications of Prentice & Breslow (1978) and Breslow et al. (1978) as the first description and use of conditional logistic regression, while economists cite the book chapter by Nobel laureate McFadden (McFadden, 1973). We describe the until-now-unrecognized use of, and way of fitting, this model in 1934 by Lionel Penrose and Ronald Fisher.

学习最优个体化治疗规则的稳健协变量平衡方法AI 解读

Abstract One of the most important problems in precision medicine is to find the optimal individualized treatment rule, which is designed to recommend treatment decisions and maximize overall clinical benefit to patients based on their individual characteristics. Typically, the expected clinical outcome is required to be estimated first, for which an outcome regression model or a propensity score model usually needs to be assumed with most existing statistical methods.…

含潜在中介变量的因果推断AI 解读

Summary Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators that are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation…

倾向得分参数约束带来的半参数效率增益AI 解读

Abstract We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where…

数据组合下的部分线性模型AI 解读

Abstract We study partially linear models when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric…

非参数结构函数与弹性的自适应估计和一致置信带AI 解读

Abstract We introduce two data-driven procedures for optimal estimation and inference in nonparametric models using instrumental variables. The first is a data-driven choice of sieve dimension for a popular class of sieve two-stage least-squares estimators. When implemented with this choice, estimators of both the structural function h0 and its derivatives (such as elasticities) converge at the fastest possible (i.e. minimax) rates in sup-norm. The second is for constructing…

战争的知识战利品？国防研发、生产率与国际溢出AI 解读

Abstract We examine the impact of government funding for R&D—and defense-related R&D in particular—on privately conducted R&D and its ultimate effect on productivity growth. We estimate longitudinal models that relate privately funded R&D to lagged government-funded R&D using industry-country level data from OECD countries and firm level data from France. To deal with the potentially endogenous allocation of government R&D funds, we use changes in predicted defense R&D as an…

企业自动化后，员工会怎样？AI 解读

Abstract We estimate the impact of firm-level automation on individual worker outcomes by combining Dutch microdata with a direct measure of automation expenditures covering all private nonfinancial sector firms. Using a novel difference-in-differences event-study design leveraging lumpy investment, we find that automation increases the probability of incumbent workers separating from their employers. Workers experience a five-year cumulative wage income loss of 9% of one…

《数据裂分：拆分单个数据点》讨论：数据裂分的若干渐近结果AI 解读

This comment was presented as part of the JSM 2024 JASA Theory and Methods invited session Data Fission: Splitting a Single Data Point.1 Leiner et al. (2025) introduce the data fission technique, w...

通过数据裂分进行经验贝叶斯估计AI 解读

We congratulate Leiner, Duan, Wasserman, and Ramdas on a stimulating article that joins an elegant method to a compelling application. Their article focuses primarily on applications to selective i...

数据裂分与抽样设计：讨论AI 解读

Leiner et al. offers a new paradigm for post-selection inference while connecting to the long-standing importance of conditioning in likelihood inference. By injecting noise to the data, the transf...

数据裂分评论：无监督学习中的聚类后推断与去偏挑战AI 解读

This article introduced data fission (Leiner et al. 2023), a valuable methodology for post-selection inference. While the article focused on the supervised setting, we explore its potential for uns...

利用重采样数据进行弱假设数据裂分AI 解读

Gaussian additive noise data fission is described in Tian and Taylor (2018) and expanded in Rasines and Young (2022). This current work, Leiner et al. (2025), expands greatly on this idea by creati...

摘要暂缺

eDNAPlus：基于 DNA 的生物多样性监测统一建模框架AI 解读

DNA-based biodiversity surveys, which involve collecting physical samples from survey sites and assaying them in the laboratory to detect species via their diagnostic DNA sequences, are increasingly being adopted for biodiversity monitoring and decision-making. The most commonly employed method, metabarcoding, combines PCR with high-throughput DNA sequencing to amplify and read “DNA barcode” sequences, generating count data indicating the number of times each DNA barcode was…

摘要暂缺

加性协方差矩阵模型：英国区域电力净需求建模AI 解读

Forecasts of regional electricity net-demand, consumption minus embedded generation, are an essential input for reliable and economic power system operation, and energy trading. While such forecasts are typically performed region by region, operations such as managing power flows require spatially coherent joint forecasts, which account for cross-regional dependencies. Here, we forecast the joint distribution of net-demand across the 14 regions constituting Great Britain’s…

立法机构的操作化研究：贝叶斯方法的方法论与实证视角AI 解读

This article extensively reviews applications, extensions, and models derived from the Bayesian ideal point estimator. We focus our attention on studies conducted in the United States as well as Latin America. First, we provide a detailed description of the Bayesian ideal point estimator. Next, we propose a new taxonomy to synthesize and frame technical developments and applications associated with the estimator in the context of the United States Congress and Latin American…

利用排序加权平均处理效应评价处理优先规则AI 解读

There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules. RATE metrics are agnostic as to how the prioritization rules were derived, and only assess how well they identify…

高维特征下的双重稳健增广模型准确度迁移推断AI 解读

Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift are common. While most research focuses on model estimation, there is limited literature on transfer inference for model accuracy despite its importance. We introduce a novel Doubly Robust Augmented Model Accuracy Transfer Inferen Ce (DRAMATIC) method for point and interval…

用于地理空间数据的神经网络AI 解读

Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a Gaussian process covariance model, encoding the spatial dependence. While nonlinear machine learning algorithms like neural networks are increasingly being used for spatial analysis, current approaches depart from the model-based setup and cannot explicitly incorporate spatial covariance. We propose NN-GLS, embedding neural…

有理克里金AI 解读

This article proposes a new kriging that has a rational form.It is shown that the generalized least squares estimator of the mean from rational kriging is much more well behaved than that of ordinary kriging.Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework.A generalized version of rational kriging is also proposed, which includes ordinary and rational kriging as special cases.Extensive simulations…

协变量分布迁移下的分布无关预测区间及因果推断应用AI 解读

Owing to its appealing distribution-free feature, conformal inference has become a popular tool for constructing prediction intervals with a desired coverage rate. In scenarios involving covariate shift, where the shift function needs to be estimated from data, many existing methods resort to data-splitting techniques. However, these approaches often lead to wider intervals and less reliable coverage rates, especially when dealing with finite sample sizes. To address these…