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Abstract Double cross-fit doubly robust (DCDR) estimators, which train nuisance function estimators on separate samples, are effective new estimators for causal functionals. We establish several novel theoretical results for them, building on recent work. We provide a structure-agnostic error analysis, which holds with generic nuisance functions and estimators. Then, we propose n-consistent DCDR estimators with undersmoothed local polynomial regression and k-Nearest…

条件可分离效应的近端因果推断AI 解读

Abstract Scientists regularly pose questions about treatment effects on outcomes conditional on a posttreatment event. However, causal inference in such settings requires care, even in perfectly executed randomized experiments. Recently, the conditional separable effect (CSE) was proposed as an interventionist estimand that corresponds to scientifically meaningful questions in these settings. However, existing results for the CSE require no unmeasured confounding between the…

网络干扰下修正处理政策的因果效应AI 解读

Abstract Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcomes of others, as is common in spatial or network data. We introduce a new class of intervention—induced modified treatment policies—which we show…

合成工具变量：从稀疏关联到稀疏因果效应AI 解读

Abstract In many observational studies, researchers are often interested in the effects of multiple exposures on a single outcome. Standard approaches for high-dimensional data, such as the Lasso, assume that the associations between the exposures and the outcome are sparse. However, these methods do not estimate causal effects in the presence of unmeasured confounding. In this paper, we consider an alternative approach that assumes the causal effects under consideration are…

用双参数敏感性分析整合随机与非随机研究AI 解读

Abstract Causal inference is vital for informed decision-making across fields such as biomedical research and social sciences. Randomized controlled trials (RCTs) are considered the gold standard for internal validity of inferences, whereas observational studies (OSs) often provide the opportunity for greater external validity. However, both data sources have inherent limitations preventing their use for broadly valid statistical inferences: RCTs may lack generalizability…

基于鞅后验的贝叶斯分位数估计与回归AI 解读

Abstract Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior…

内生免赔额保险合同定价的Copula面板数据模型AI 解读

Summary Insurers adjust premiums for renewing policyholders based on past claims, a practice known as experience rating. This paper examines experience rating in insurance contracts with deductibles, a key instrument for mitigating inefficiencies arising from information asymmetry between policyholders and insurers. We propose a copula-based panel data model that accounts for the endogeneity of deductible choice, particularly when the assumption of contemporaneous exogeneity…

分期实施政策的合成控制推断AI 解读

Summary Many policies are replicated by other policymakers at different times. We introduce a synthetic control methodology to study policies with staggered adoption. Our method estimates the dynamic average treatment effects on the treated using variation introduced by the staggered adoption of policies. Our method gives asymptotically unbiased estimators of many interesting quantities and delivers asymptotically valid inference. Applying the method to intergovernmental…

存在溢出效应时合成控制方法的识别与贝叶斯推断AI 解读

Summary The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption, ruling out spillover effects. However, interventions often affect not only treated but also untreated units. This study proposes a novel panel data method that extends the standard SCM to account for spillovers and estimate both treatment and spillover effects. The…

用于中介效应检验的有限样本增广似然比检验AI 解读

Summary Testing for mediation suffers from low power near the origin. This can be addressed by augmenting the likelihood ratio critical region as discussed in previous work within an asymptotic framework. In this paper we consider exact distributions of test statistics and use this to derive optimal augmentation regions for arbitrary sample sizes. The resulting test is very simple and reliable, making it relevant for empirical research, even with a very small number of…

固定 T 异质性面板模型的双向均值组估计AI 解读

Summary We consider a correlated random coefficient panel data model with two-way fixed effects and interactive fixed effects in a fixed T framework. The model allows slope coefficients to be arbitrarily correlated with the regressors, accommodating flexible forms of heterogeneity. We propose a two-way mean group estimator for the expected value of the slope coefficient and propose a leave-one-out jackknife method for valid inference. We apply our new methods to examine the…

多结果变量的合成控制法AI 解读

Summary We generalize the synthetic control (SC) method to a multiple-outcomes framework in which the conventional pretreatment time dimension is supplemented with the extra dimension of related outcomes in computing the SC weights. This generalization reduces the risk of overfitting and improves the reliability of the SC method, particularly when the number of pretreatment periods is small. To illustrate our method, we provide a new perspective on the classic SC application…

分位数网络向量自回归中的双向同质性分组AI 解读

We propose a two-way grouped network quantile (TGNQ) autoregression model for time series data observed on networks with substantial heterogeneity and complex directional interactions. Motivated by prior studies on network effects in directed networks, our model assigns each node two latent group memberships to flexibly capture asymmetric and heterogeneous interactions among users. These memberships, along with model parameters, can be consistently estimated using the…

空间动态面板的再中心化矩估计量AI 解读

This paper proposes a recentered method of moments (RMM) approach for estimating spatial dynamic panel data (SDPD) models with both individual and time fixed effects. This approach directly exploits the correlation of regressors and disturbances, where heteroskedasticity may exist across units and/or over time. It is computationally tractable and can handle situations where other methods are not applicable. We show that the proposed estimator is consistent and asymptotically…

识别失败与模型设定正确性的协调检验AI 解读

We develop a specification test for GMM and Minimum Distance (MD) inference that is: (i) robust to weak identification; (ii) easy to implement and broadly applicable; and (iii) has meaningful power. Our specification test is based on a conditional inference approach: information about the existence of strongly identified directions in the parameter space is exploited to define a data-dependent critical value, which, in turn, increases the power of the resulting specification…

测地线双重差分AI 解读

Summary Difference-in-differences (DID) is a widely used quasi-experimental design for causal inference, traditionally applied to scalar or Euclidean outcomes, while extensions to outcomes residing in non-Euclidean spaces remain limited. Existing methods for such outcomes have primarily focused on univariate distributions, leveraging linear operations in the space of quantile functions, but these approaches cannot be directly extended to outcomes in general metric spaces. In…

本地分布式多网络社区检测的隐私保护迁移学习AI 解读

Modern applications increasingly involve highly sensitive network data, where raw edges cannot be shared due to privacy constraints. We propose \texttt{TransNet}, a new spectral clustering-based transfer learning framework that improves community detection on a \emph{target network} by leveraging heterogeneous, locally stored, and privacy-preserved auxiliary \emph{source networks}. Our focus is the \textit{local differential privacy} regime, in which each local data provider…

高维分位数回归的无模型特征筛选与错误发现控制AI 解读

High-dimensional quantile regressions are powerful tools for capturing the diverse relationships between predictors and response variables across different quantile levels. However, existing feature screening methods for quantile regressions often rely on restrictive model assumptions, limiting their applicability in complex data settings. In this paper, we propose a novel model-free feature screening framework for high-dimensional quantile regressions, leveraging a novel…

STRAP：利用随机投影的多元同质性平滑检验AI 解读

In this paper, we propose STRAP (Smooth Tests using RAdom Projections), projected smooth tests for the equality of two multivariate distributions. We employ random projections to map the multivariate data into one-dimensional data and transform the projected data via the probability integral transformation. Under the null hypothesis of homogeneity, the transformed data follow the standard uniform distribution, reducing the problem to testing for uniformity. Building on…

多个内生解释变量下 2SLS 的稳健弱工具变量检验AI 解读

Abstract We develop a test for instrument strength based on the bias of two-stage least squares (2SLS) that (1) generalizes Stock and Yogo’s and Sanderson and Windmeijer’s tests to be robust to heteroskedasticity and autocorrelation, and (2) extends Montiel Olea and Pflueger’s robust test for models with a single endogenous regressor to multiple endogenous regressors. Our test can be based either on an absolute bias criterion or on the 2SLS bias relative to a worst-case…

失业、失业保险与健康：来自巴西的证据AI 解读

Abstract We study the health impacts of job loss and unemployment insurance (UI) among Brazilian workers, using unique administrative data linking employment, hospitalizations, health insurance, and mortality records over 17 years. Leveraging mass layoffs for identification, we find that job loss raises hospitalization (+33%) and mortality (+23%) risks for men, with no significant effects for women. Impacts are pervasive across age, income, and education levels, with…

重返办公室政策与员工任职年限分布AI 解读

Abstract Debates over return-to-office mandates have intensified since the COVID-19 pandemic ended, though their economic implications remain poorly understood. Using large-scale resume data, we analyze how these policies affect employee tenure and seniority at three large U.S. tech companies: Microsoft, SpaceX, and Apple. Employing a distributional synthetic controls framework, we estimate a reduction in tenure and seniority at firms returning to the office. We extend the…

均衡扩散模型中从微观到宏观的推断AI 解读

Abstract We quantify the benefits of better firm-to-firm matching in an aggregate diffusion model where individuals acquire profitable knowledge from others. We estimate the model using a small-scale program in Kenya that creates new opportunities for firm managers to interact. Aggregate gains from the program depend on the relative importance of meeting a high-knowledge firm versus learning within that meeting. We show how moments from the intervention identify these…

基于二次型的再随机化统一框架AI 解读

When designing a randomized experiment, one way to ensure treatment and control groups exhibit similar covariate distributions is to randomize treatment until some prespecified level of covariate balance is satisfied; this strategy is known as rerandomization. Most rerandomization methods utilize balance metrics based on a quadratic form vTAv, where v is a vector of covariate mean differences and A is a positive semi-definite matrix. In this work, we derive general results…