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Instrumental‐variable estimation of exponential‐regression models with two‐way fixed effects with an application to gravity equations

含双向固定效应的指数回归工具变量估计及引力方程应用

Summary This paper introduces instrumental‐variable estimators for exponential‐regression models that feature two‐way fixed effects. These techniques allow us to develop a theory‐consistent approach to the estimation of cross‐sectional gravity equations that can accommodate the endogeneity of policy variables. We apply this approach to a dataset in which the policy decision of interest is the engagement in a free‐trade agreement. We explore ways to exploit the transitivity…

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Identification of dynamic latent factor models of skill formation with translog production

具有超越对数生产技术的动态潜在因子技能形成模型识别

Summary In this paper, we highlight an important property of the translog production function for the identification of treatment effects in a model of latent skill formation. We show that when using a translog specification of the skill technology, properly anchored treatment effect estimates are invariant to any location and scale normalizations of the underlying measures. By contrast, when researchers assume a CES production function and impose standard location and scale…

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Covariate distribution balance via propensity scores

通过倾向得分实现协变量分布平衡

Summary This paper proposes new estimators for the propensity score that aim to maximize the covariate distribution balance among different treatment groups. Heuristically, our proposed procedure attempts to estimate a propensity score model by making the underlying covariate distribution of different treatment groups as close to each other as possible. Our estimators are data‐driven and can be used to estimate different treatment effect parameters under different…

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Heterogeneous coefficients, control variables and identification of multiple treatment effects

异质系数、控制变量与多重处理效应识别

Summary Multi-dimensional heterogeneity and endogeneity are important features of models with multiple treatments. We consider a heterogeneous coefficients model where the outcome is a linear combination of dummy treatment variables, with each variable representing a different kind of treatment. We use control variables to give necessary and sufficient conditions for identification of average treatment effects. With mutually exclusive treatments we find that, provided the…

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Searching for robust associations with a multi-environment knockoff filter

利用多环境knockoff筛选寻找稳健关联

Summary In this article we develop a method based on model-X knockoffs to find conditional associations that are consistent across environments, while controlling the false discovery rate. The motivation for this problem is that large datasets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations replicated under different conditions may be more interesting.…

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On the relative efficiency of the intent-to-treat Wilcoxon–Mann–Whitney test in the presence of noncompliance

存在不依从时意向治疗Wilcoxon–Mann–Whitney检验的相对效率

Summary A general framework is set up to study the asymptotic properties of the intent-to-treat Wilcoxon–Mann–Whitney test in randomized experiments with nonignorable noncompliance. Under location-shift alternatives, the Pitman efficiencies of the intent-to-treat Wilcoxon–Mann–Whitney and $t$ tests are derived. It is shown that the former is superior if the compliers are more likely to be found in high-density regions of the outcome distribution or, equivalently, if the…

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Regression-based causal inference with factorial experiments: estimands, model specifications and design-based properties

析因实验中的回归因果推断:目标量、模型设定与基于设计的性质

Summary Factorial designs are widely used because of their ability to accommodate multiple factors simultaneously. Factor-based regression with main effects and some interactions is the dominant strategy for downstream analysis, delivering point estimators and standard errors simultaneously via one least-squares fit. Justification of these convenient estimators from the design-based perspective requires quantifying their sampling properties under the assignment mechanism…

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Instrumental Variable Identification of Dynamic Variance Decompositions

动态方差分解的工具变量识别

Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving-average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various…

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Generalization error bounds of dynamic treatment regimes in penalized regression-based learning

惩罚回归学习动态治疗方案的泛化误差界

A dynamic treatment regime (DTR) is a sequence of decision rules, one per stage of intervention, that maps up-to-date patient information to a recommended treatment. Discovering an appropriate DTR for a given disease is a challenging issue especially when a large set of prognostic variables are observed. To address this problem, we propose penalized regression-based learning methods with l1 penalty to estimate the optimal DTR that would maximize the expected outcome if…

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Random graph asymptotics for treatment effect estimation under network interference

网络干扰下处理效应估计的随机图渐近理论

The network interference model for treatment effect estimation places experimental units at the vertices of an undirected exposure graph, such that treatment assigned to one unit may affect the outcome of another unit if and only if these two units are connected by an edge. This model has recently gained popularity as means of incorporating interference effects into the Neyman–Rubin potential outcomes framework; and several authors have considered estimation of various…

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Do Firms Respond to Gender Pay Gap Transparency?

企业会对性别薪酬差距透明化作出反应吗?

ABSTRACT We examine the effect of pay transparency on the gender pay gap and firm outcomes. Using a 2006 legislation change in Denmark that requires firms to provide gender‐disaggregated wage statistics, detailed employee‐employer administrative data, and difference‐in‐differences and difference‐in‐discontinuities designs, we find that the law reduces the gender pay gap, primarily by slowing wage growth for male employees. The gender pay gap declines by 2 percentage points,…

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IDENTIFICATION AND ESTIMATION IN A CORRELATED RANDOM COEFFICIENTS TRANSFORMATION MODEL

相关随机系数变换模型的识别与估计

This study examines identification and estimation in a correlated random coefficients (CRC) model with an unknown transformation of the dependent variable, namely $\lambda \left (Y^{*}\right)=B_{0}+X^{\prime }B$ , where the latent outcome $Y^{*}$ may be subject to a certain kind of censoring mechanism, $\lambda (\cdot)$ is an unknown, one-to-one monotone function, and the random coefficients $\left (B_{0},B\right)$ are allowed to be correlated with one or several components…

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High-Dimensional Mediation Analysis for Selecting DNA Methylation Loci Mediating Childhood Trauma and Cortisol Stress Reactivity

高维中介分析:筛选连接童年创伤与皮质醇应激反应的 DNA 甲基化位点

Childhood trauma tends to influence cortisol stress reactivity through the mediating effects of DNA methylation. Houtepen et al. conducted a study to investigate the role of DNA methylation in cortisol stress reactivity and its relationship with childhood trauma. The study collected a dataset consisting of 385,882 DNA methylation loci, cortisol stress reactivity, one-dimensional score on a childhood trauma questionnaire and several covariates for 85 healthy individuals. Of…

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Nonparametric Tests of the Causal Null With Nondiscrete Exposures

非离散暴露下因果零假设的非参数检验

In many scientific studies, it is of interest to determine whether an exposure has a causal effect on an outcome. In observational studies, this is a challenging task due to the presence of confounding variables that affect both the exposure and the outcome. Many methods have been developed to test for the presence of a causal effect when all such confounding variables are observed and when the exposure of interest is discrete. In this article, we propose a class of…

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Minimax Efficient Random Experimental Design Strategies With Application to Model-Robust Design for Prediction

极小极大高效随机实验设计及其模型稳健预测应用

In game theory and statistical decision theory, a random (i.e., mixed) decision strategy often outperforms a deterministic strategy in minimax expected loss. As experimental design can be viewed as a game pitting the Statistician against Nature, the use of a random strategy to choose a design will often be beneficial. However, the topic of minimax-efficient random strategies for design selection is mostly unexplored, with consideration limited to Fisherian randomization of…

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Sensitivity Analysis via the Proportion of Unmeasured Confounding

基于未测量混杂比例的敏感性分析

In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this article, we take a novel approach whereby…

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Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design

无金标准病例定义下的疟疾疫苗效力估计:孟德尔析因设计

Accurate estimates of malaria vaccine efficacy require a reliable definition of a malaria case. However, the symptoms of clinical malaria are unspecific, overlapping with other childhood illnesses. Additionally, children in endemic areas tolerate varying levels of parasitemia without symptoms. Together, this makes finding a gold-standard case definition challenging. We present a method to identify and estimate malaria vaccine efficacy that does not require an observable…

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Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

利用多类别角度学习估计删失数据下的最优动态治疗方案

An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches…

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Markov Neighborhood Regression for High-Dimensional Inference

用于高维推断的马尔可夫邻域回归

This article proposes an innovative method for constructing confidence intervals and assessing p-values in statistical inference for high-dimensional linear models. The proposed method has successfully broken the high-dimensional inference problem into a series of low-dimensional inference problems: For each regression coefficient βi, the confidence interval and p-value are computed by regressing on a subset of variables selected according to the conditional independence…

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Standard Synthetic Control Methods: The Case of Using All Preintervention Outcomes Together With Covariates

标准合成控制:同时使用全部干预前结果与协变量的情形

It is becoming increasingly popular in applications of standard synthetic control methods to include the entire pretreatment path of the outcome variable as economic predictors. We demonstrate both theoretically and empirically that using all outcome lags as separate predictors renders all other covariates irrelevant in such settings. This finding holds irrespective of how important these covariates are for accurately predicting posttreatment values of the outcome,…

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Synthetic Control Estimation Beyond Comparative Case Studies: Does the Minimum Wage Reduce Employment?

超越比较案例研究的合成控制估计:最低工资是否减少就业?

Panel data are often used in empirical work to account for fixed additive time and unit effects. The synthetic control estimator relaxes the assumption of additive fixed effects for comparative case studies in which a treated unit adopts a single policy. This article generalizes the synthetic control estimator to estimate parameters associated with multiple discrete or continuous explanatory variables, jointly estimating the parameters and synthetic controls for each unit. I…

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Local Polynomial Order in Regression Discontinuity Designs

断点回归设计中的局部多项式阶数选择

Treatment effect estimates in regression discontinuity (RD) designs are often sensitive to the choice of bandwidth and polynomial order, the two important ingredients of widely used local regression methods. While Imbens and Kalyanaraman and Calonico, Cattaneo, and Titiunik provided guidance on bandwidth, the sensitivity to polynomial order still poses a conundrum to RD practitioners. It is understood in the econometric literature that applying the argument of bias reduction…

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The PCDID Approach: Difference-in-Differences When Trends Are Potentially Unparallel and Stochastic

PCDID:趋势可能非平行且随机时的主成分双重差分

We develop a class of regression-based estimators, called Principal Components Difference-in-Differences (PCDID) estimators, for treatment effect estimation. Analogous to a control function approach, PCDID uses factor proxies constructed from control units to control for unobserved trends, assuming that the unobservables follow an interactive effects structure. We clarify the conditions under which the estimands in this regression-based approach represent useful causal…

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Nonparametric Instrumental Regression With Right Censored Duration Outcomes

右删失持续时间结果的非参数工具变量回归

This article analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global…

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