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中高维协变量下完全随机实验的去偏回归调整AI 解读

Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to include them in covariate adjustments for more accurate treatment effect estimation. In this paper, we investigate this problem under the randomization-based framework, i.e., that the covariates and potential outcomes of all experimental candidates are assumed as deterministic quantities and the…

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纵向网络数据的半参数建模与分析AI 解读

We introduce a semiparametric latent space model for analyzing longitudinal network data. The model consists of a static latent space component and a time-varying node-specific baseline component. We develop a semiparametric efficient score equation for the latent space parameter by adjusting for the baseline nuisance component. Estimation is accomplished through a one-step update estimator and an appropriately penalized maximum likelihood estimator. We derive oracle error…

存在期限结构时的稳健双重差分分析AI 解读

For variables with a term structure, the standard difference-in-differences (DiD) model is predisposed toward misspecification, even under random assignment, because of heterogeneity over the maturity spectrum and imperfect matching between treated and control units. Estimated treatment effects that are false, biased, or hard to interpret become a concern. Neither unit fixed effects nor standard term-structure controls resolve the problem. Solutions that overcome imperfect…

污染资产再配置：环境……的影响（原题截断）AI 解读

This paper investigates how firms’ pollution incentives are influenced by their ability to divest polluted assets. My empirical setting is a major reform that exempts purchasers from liability for past contamination. Using a difference-in-differences framework, I find that the reform reduces toxic emissions, lowers bankruptcy risk, and increases firm value. Cross-sectional tests show that the decline in emissions is driven by firms with weaker financial health and fewer…

动态二元选择面板数据模型的半参数估计AI 解读

We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model under consideration has the same random utility framework as in Honoré and Kyriazidou (2000, Econometrica 68, 839–874). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions on the error distribution, the (point) identification of the model…

内生转换模型中的异质性与异方差：医生建议对热量摄入的影响AI 解读

ABSTRACT We describe two‐step control function estimation procedures for estimating average treatment effects in constant coefficient endogenous switching models with a binary treatment. We allow for additional heterogeneity by allowing for heteroskedasticity in the latent error in the treatment assignment equation. We apply our estimation procedures to evaluate the causal effect of physician advice on calorie consumption using National Health and Nutritional Examination…

工作场所同伴对未来工资的影响AI 解读

ABSTRACT This paper examines workplace peer effects in two directions, leveraging employer‐employee data for Italy. First, using a novel estimation approach and addressing endogenous worker‐peer sorting, we estimate that a 10% increase in peer quality raises one's wage by 1.8% in the next year. The effect declines to 0.7% after 5 years. Second, in an event study around mobility episodes, we quantify wage changes associated with the entry and leave of high‐quality and…

含固定效应的动态有序Logit模型AI 解读

Abstract We study a fixed-T panel data logit model for ordered outcomes that accommodates fixed effects and state dependence. We provide identification results for the autoregressive parameter, regression coefficients, and the threshold parameters in this model. Our results require only four observations on the outcome variable. We provide conditions under which a composite conditional maximum likelihood estimator is consistent and asymptotically normal. We use our estimator…

有限个异质聚类下的置换推断AI 解读

Abstract I introduce a simple permutation procedure to test conventional (nonsharp) hypotheses about the effect of a binary treatment in the presence of a finite number of large, heterogeneous clusters when the treatment effect is identified by comparisons across clusters. The procedure asymptotically controls size by applying a level-adjusted permutation test to a suitable statistic. The adjusted permutation test is easy to implement in practice and performs well at…

删失事件时间数据中因果净获益的去偏学习AI 解读

Summary Cox regression is the default approach to evaluating the (relative) effect of two treatments on a survival endpoint. This standard framework has nonetheless been criticized for its canonical effect measure, the hazard ratio, having a subtle interpretation, thereby hindering policy-making. This in turn has prompted interest in other effects measures, such as the difference in restricted mean survival time, the net benefit and the win ratio, which have become…

有序结果中因果必要性概率的识别与界限AI 解读

Summary Although the existing causal inference literature focuses on the forward-looking perspective by estimating effects of causes, the backward-looking perspective can provide insights into causes of effects. In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment, given the observed treatment and outcome. Most existing results focus on binary outcomes. Motivated by applications with…

交叉随机效应 probit 模型的一致且可扩展复合似然估计AI 解读

Summary Estimation of crossed random effects models commonly incurs computational costs that grow faster than linearly in the sample size $ N $, often as fast as $ \Omega(N^{3/2}) $, making them unsuitable for large datasets. For non-Gaussian responses, integrating out the random effects to obtain a marginal likelihood poses significant challenges, especially for high-dimensional integrals for which the Laplace approximation may not be accurate. In this article we develop a…

协变量自适应随机化临床试验中的一般协变量调整AI 解读

Abstract In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we…

提高平均处理效应跨总体迁移的估计效率AI 解读

Abstract We develop flexible, semiparametric estimators of the average treatment effect transported to a new target population, which offer potential efficiency gains. Transport may be of value when the average treatment effect may differ across populations. We consider the setting where differences in the average treatment effect are due to differences in the distribution of effect modifiers, baseline covariates that modify the treatment effect. First, we propose a…

广义线性网络特征模型中用于维度选择的尖峰与厚尾先验AI 解读

Summary Latent space models are often used to model network data by embedding a network’s nodes into a low-dimensional latent space; however, choosing the dimension of this space remains a challenge. To this end, we begin by formalizing a class of latent space models we call generalized linear network eigenmodels that can model various edge types (binary, ordinal, nonnegative continuous) found in scientific applications. This model class subsumes the traditional eigenmodel…

删失数据下纵向替代指标的稳健评估AI 解读

Abstract The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g. fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the…

因果中介分析：选择与渐近有效推断AI 解读

Abstract Researchers are often interested in learning not only the effect of treatments on outcomes, but also the mechanisms that transmit these effects. A mediator is a variable that is affected by treatment and subsequently affects outcome. Existing methods for penalized mediation analyses may lead to ignoring important mediators and either assume that finite-dimensional linear models are sufficient to remove confounding bias, or perform no confounding control at all. In…

Engression：从分布回归视角研究外推AI 解读

Abstract Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree ensemble based quantile regression. We propose a neural network-based distributional regression methodology called ‘engression’. An engression model is generative in the sense that we can sample from the fitted conditional distribution and is also suitable for high-dimensional outcomes. Furthermore, we find that…

含干预的半机制流行病模型：频率学派推断AI 解读

Abstract The effect of public health interventions on an epidemic are often estimated by adding the intervention to epidemic models. During the Covid-19 epidemic, numerous papers used such methods for making scenario predictions. The majority of these papers use Bayesian methods to estimate the parameters of the model. In this article, we show how to use frequentist methods for estimating these effects which avoids having to specify prior distributions. We also use…

没有免费午餐？经由专家中介销售企业的福利分析AI 解读

Abstract We study how firms target and influence expert intermediaries. In our context, pharmaceutical manufacturers provide payments to physicians during promotional interactions. We develop an identification strategy based on plausibly exogenous variation in payments driven by differential exposure to spillovers from AMC CoI policies. Using a case study of an important class of cardiovascular drugs, we estimate heterogeneous effects of payments on prescribing, with firms…

从面板数据识别网络联系：理论及税收竞争应用AI 解读

Abstract Social interactions determine many economic behaviours, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social…

招聘广告中的性别偏好与工作场所性别多样性AI 解读

Abstract In Spring 2005, the Ombud for Equal Treatment in Austria launched a campaign notifying employers and newspapers that gender preferences in job ads were illegal. At the time, over 40% of vacancies on the nation’s largest job board stated a gender preference; within a year the rate fell below 5%. We merge job board vacancies and employer records to study how the campaign affected hiring choices and the gender diversity of occupations and workplaces. Using pre-campaign…

反馈与学习：改判对司法决策的因果影响AI 解读

Abstract Do judges respond to reversals of their decisions? Using random assignment of cases across two stages of the criminal justice system in Norway and a novel dataset linking trial court decisions to reversals in appeals courts, we provide causal evidence on feedback effects in judicial decision-making. By exploiting differences in the tendencies of randomly assigned appeal panels to reverse trial court decisions, we show that trial court judges who receive a reversal…

兼顾多样性与异质受欢迎程度的超图建模AI 解读

While relations among individuals make an important part of data with scientific and business interests, existing statistical modeling of relational data has mainly been focusing on dyadic relations, that is, those between two individuals.This article addresses the less studied, though commonly encountered, polyadic relations that can involve more than two individuals.In particular, we propose a new latent space model for hypergraphs using determinantal point processes,…