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利用负向对照识别无效工具变量下的因果效应AI 解读

Summary Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. In this paper, we show how one can potentially identify causal effects under violations of these assumptions by harnessing a negative control population or outcome. This strategy allows one to leverage subpopulations for whom the exposure is degenerate, and requires that the…

论 Rosenbaum 的秩匹配估计量AI 解读

Summary In two influential contributions, Rosenbaum (2005, 2020a) advocated for using the distances between componentwise ranks, instead of the original data values, to measure covariate similarity when constructing matching estimators of average treatment effects. While the intuitive benefits of using covariate ranks for matching estimation are apparent, there is no theoretical understanding of such procedures in the literature. We fill this gap by demonstrating that…

通过反演估计方程开展分位数因果推断AI 解读

Summary The causal inference literature frequently focuses on estimating the mean of the potential outcome, whereas quantiles of the potential outcome may carry important additional information. We propose an inverse estimating equation framework to generalize a wide class of causal inference solutions from estimating the mean of the potential outcome to its quantiles. We assume that a moment function is available to identify the mean of the threshold-transformed potential…

半监督分布学习AI 解读

Abstract This study addresses the challenge of distribution estimation and inference in a semi-supervised setting. In contrast to prior research focusing on parameter inference, this work explores the complexities of semi-supervised distribution estimation, particularly the uniformity problem inherent in functional processes. To tackle this issue, we introduce a versatile framework designed to extract valuable information from unlabelled data by approximating a conditional…

随机回归量下最小二乘推断对未知相关误差的稳健性AI 解读

Abstract Linear regression is arguably the most widely used statistical method. With fixed regressors and correlated errors, the conventional wisdom is to modify the variance-covariance estimator to accommodate the known correlation structure of the errors. We depart from existing literature by showing that with random regressors, linear regression inference is robust to correlated errors with unknown correlation structure. The existing theoretical analyses for linear…

信息性观测时点下的函数主成分分析AI 解读

Summary Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serve as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis, often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven…

平滑参数的自由度AI 解读

Summary The smoothing parameters in a semiparametric model are estimated based on criteria such as generalized cross-validation or restricted maximum likelihood. As these parameters are estimated in a data-driven manner, they influence the degrees of freedom of a semiparametric model, based on Stein’s lemma. This allows us to associate parts of the degrees of freedom of a semiparametric model with the smoothing parameters. A framework is introduced that enables these degrees…

用于错误发现率控制的自适应零假设比例估计量AI 解读

Abstract The false discovery rate is a commonly used criterion in multiple testing, and the Benjamini–Hochberg procedure is a standard approach to false discovery rate control. To increase its power, adaptive Benjamini–Hochberg procedures, that use estimates of the null proportion, have been proposed. A particularly popular approach being that based on Storey’s estimator. The performance of Storey’s estimator hinges on a critical hyperparameter, such that a pre-fixed…

利用 e 过程检验均值与方差AI 解读

Summary We address the problem of testing the conditional mean and conditional variance for nonstationary data. We build e-values and p-values for four types of nonparametric composite hypothesis with specified mean and variance as well as other conditions on the shape of the data-generating distribution. These shape conditions include symmetry, unimodality and their combination. Using the obtained e-values and p-values, we construct tests via e-processes, also known as…

提升核两样本检验的功效AI 解读

Summary The kernel two-sample test based on the maximum mean discrepancy is one of the most popular methods for detecting differences between two distributions over general metric spaces. In this paper we propose a method to boost the power of the kernel test by combining maximum mean discrepancy estimates over multiple kernels using their Mahalanobis distance. We derive the asymptotic null distribution of the proposed test statistic and use a multiplier bootstrap approach…

网络数据的局部 bootstrapAI 解读

Abstract In network analysis, one frequently needs to conduct inference for network parameters based on a single observed network. Since the sampling distribution of the statistic is often unknown, one has to rely on the bootstrap. However, because of the complex dependence structure among vertices, existing bootstrap methods often yield unsatisfactory performance, especially for small or moderate sample sizes. Here we propose a new network bootstrap procedure, termed the…

Chatterjee 秩相关系数的简单 bootstrapAI 解读

Abstract We prove that an m-out-of-n bootstrap procedure for Chatterjee’s rank correlation is consistent whenever asymptotic normality of Chatterjee’s rank correlation can be established. In particular, we prove that m-out-of-n bootstrap works for continuous as well as discrete data with independent coordinates; furthermore, simulations indicate that it also performs well for discrete data with dependent coordinates, and that it outperforms alternative estimation methods.…

网络纠缠导致非个体化处理分配时的因果效应估计AI 解读

Summary In many observational studies, the treatment assignment mechanism is not individualistic, as it allows the probability of treatment of a unit to depend on quantities beyond the unit’s covariates. In such settings, unit treatments may be entangled in complex ways. In this article, we consider a particular instance of this problem where the treatments are entangled by a social network among units. For instance, when studying the effects of peer interaction on a social…

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…

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…

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…

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...

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…

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…