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Identification and Estimation of Triangular Simultaneous Equations Models Without Additivity

无加性假设的三角联立方程模型:识别与估计

This paper uses control variables to identify and estimate models with nonseparable, multidimensional disturbances. Triangular simultaneous equations models are considered, with instruments and disturbances that are independent and a reduced form that is strictly monotonic in a scalar disturbance. Here it is shown that the conditional cumulative distribution function of the endogenous variable given the instruments is a control variable. Also, for any control variable,…

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Social Connections and Incentives in the Workplace: Evidence From Personnel Data

工作场所的社会关系与激励:来自人事数据的证据

We present evidence on the effect of social connections between workers and managers on productivity in the workplace. To evaluate whether the existence of social connections is beneficial to the firm's overall performance, we explore how the effects of social connections vary with the strength of managerial incentives and worker's ability. To do so, we combine panel data on individual worker's productivity from personnel records with a natural field experiment in which we…

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Testing Models With Multiple Equilibria by Quantile Methods

利用分位数方法检验多重均衡模型

Abstract. This paper proposes a method for testing complementarities between explanatory and dependent variables in a large class of economic models. The proposed test is based on the monotone comparative statics (MCS) property of equilibria. Our main result is that MCS produces testable implications on the (small and large) quantiles of the dependent variable, despite the presence of multiple equilibria. The key features of our approach are: (1) we work with a nonparametric…

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Multiply Robust Inference for Statistical Interactions

统计交互作用的多重稳健推断

A primary focus of an increasing number of scientific studies is to determine whether two exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in the interaction, this approach is not entirely satisfactory because it is prone to (possibly severe) bias when the main exposure effects or…

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Does Finasteride Affect the Severity of Prostate Cancer? A Causal Sensitivity Analysis

非那雄胺是否影响前列腺癌严重程度:因果敏感性分析

In 2003 Thompson and colleagues reported that daily use of finasteride reduced the prevalence of prostate cancer by 25% compared to placebo. These results were based on the double-blind randomized Prostate Cancer Prevention Trial (PCPT) which followed 18,882 men with no prior or current indications of prostate cancer annually for seven years. Enthusiasm for the risk reduction afforded by the chemopreventative agent and adoption of its use in clinical practice, however, was…

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Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects

早期干预效果的多重推断与性别差异:三项实验的重新评估

The view that the returns to educational investments are highest for early childhood interventions is widely held and stems primarily from several influential randomized trials—Abecedarian, Perry, and the Early Training Project—that point to super-normal returns to early interventions. This article presents a de novo analysis of these experiments, focusing on two core issues that have received limited attention in previous analyses: treatment effect heterogeneity by gender…

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Estimation of Controlled Direct Effects

受控直接效应的估计

Summary When regression models adjust for mediators on the causal path from exposure to outcome, the regression coefficient of exposure is commonly viewed as a measure of the direct exposure effect. This interpretation can be misleading, even with a randomly assigned exposure. This is because adjustment for post-exposure measurements introduces bias whenever their association with the outcome is confounded by more than just the exposure. By the same token, adjustment for…

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Early Marriage, Age of Menarche, and Female Schooling Attainment in Bangladesh

孟加拉国的早婚、初潮年龄与女性受教育程度

Using data from rural Bangladesh, we explore the hypothesis that women attain less schooling as a result of social and financial pressure to marry young. We isolate the causal effect of marriage timing using age of menarche as an instrumental variable. Our results indicate that each additional year that marriage is delayed is associated with 0.22 additional year of schooling and 5.6 percent higher literacy. Delayed marriage is also associated with an increase in use of…

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How Does Financing Impact Investment? The Role of Debt Covenants

融资如何影响投资?债务契约条款的作用

ABSTRACT We identify a specific channel (debt covenants) and the corresponding mechanism (transfer of control rights) through which financing frictions impact corporate investment. Using a regression discontinuity design, we show that capital investment declines sharply following a financial covenant violation, when creditors use the threat of accelerating the loan to intervene in management. Further, the reduction in investment is concentrated in situations in which agency…

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Dynamic Treatment Assignment

动态处理分配

We discuss estimation of treatment effects when the timing of treatment is the outcome of a stochastic process. We show that the duration framework in discrete time provides a fertile ground for effect evaluations. We suggest easy-to-use nonparametric survival function matching estimators that can be used to estimate the time profile of the treatment.We study the small-sample properties of the proposed estimators and apply one of them to evaluate the effects of an employment…

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Supremum weighted log-rank test and sample size for comparing two-stage adaptive treatment strategies

比较两阶段自适应治疗策略的上确界加权对数秩检验与样本量

In two-stage adaptive treatment strategies, patients receive an induction treatment followed by a maintenance therapy, given that the patient responded to the induction treatment they received. To test for a difference in the effects of different induction and maintenance treatment combinations, a modified supremum weighted log-rank test is proposed. The test is applied to a dataset from a two-stage randomized trial and the results are compared to those obtained using a…

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Adjustment uncertainty in effect estimation

效应估计中的调整不确定性

Often there is substantial uncertainty in the selection of confounders when estimating the association between an exposure and health. We define this type of uncertainty as `adjustment uncertainty'. We propose a general statistical framework for handling adjustment uncertainty in exposure effect estimation for a large number of confounders, we describe a specific implementation, and we develop associated visualization tools. Theoretical results and simulation studies show…

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Improving Semiparametric Estimation by Using Surrogate Data

利用替代数据改进半参数估计

Summary The paper considers estimating a parameter β that defines an estimating function U(y, x, β) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for β depends critically on correctly specifying the conditional expectation of U given the surrogate and the covariate.…

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Empirical Likelihood-Based Estimation of the Treatment Effect in a Pretest–Posttest Study

前测后测研究中基于经验似然的处理效应估计

The pretest-posttest study design is commonly used in medical and social science research to assess the effect of a treatment or an intervention. Recently, interest has been rising in developing inference procedures that improve efficiency while relaxing assumptions used in the pretest-posttest data analysis, especially when the posttest measurement might be missing. In this article we propose a semiparametric estimation procedure based on empirical likelihood (EL) that…

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War and Wages

战争与工资

An instrument manipulates a treatment that it does not entirely control, but the instrument affects the outcome only indirectly through its manipulation of the treatment. The idealized prototype is the randomized encouragement design, in which subjects are randomly assigned to receive either encouragement to accept the treatment or no such encouragement, but not all subjects comply by doing what they are encouraged to do, and the situation is such that only the treatment…

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R&D and subsidies at the firm level: an application of parametric and semiparametric two‐step selection models

企业层面的研发与补贴:参数和半参数两步选择模型的应用

Abstract This paper analyzes the effect of public R&D subsidies on firms' private R&D investment per employee and new product sales in German manufacturing. Parametric and semiparametric two‐step selection models are applied to this evaluation problem. The results show that the average treatment effect on the treated firms' R&D intensity is positive. The estimated effects are robust with respect to the different selection models. Further results show that publicly induced…

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The wages of BMI: Bayesian analysis of a skewed treatment–response model with nonparametric endogeneity

BMI 与工资:偏态处理—响应模型及非参数内生性的贝叶斯分析

Abstract We generalize the specifications used in previous studies of the effect of body mass index (BMI) on earnings by allowing the potentially endogenous BMI variable to enter the log wage equation nonparametrically. We introduce a Bayesian posterior simulator for fitting our model that permits a nonparametric treatment of the endogenous BMI variable, flexibly accommodates skew in the BMI distribution, and whose implementation requires only Gibbs steps. Using data from…

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Efficient and Doubly Robust Imputation for Covariate-Dependent Missing Responses

协变量依赖的响应缺失下高效且双重稳健的插补

In this article we study a well-known response missing-data problem. Missing data is an ubiquitous problem in medical and social science studies. Imputation is one of the most popular methods for dealing with missing data. The most commonly used imputation that makes use of covariates is regression imputation, in which the regression model can be parametric, semiparametric, or nonparametric. Parametric regression imputation is efficient but is not robust against…

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Learning Causal Bayesian Network Structures From Experimental Data

从实验数据学习因果贝叶斯网络结构

We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Order-space Markov chain Monte Carlo, equi-energy sampling, importance weighting, and stream-based computation are combined to create a fast algorithm for learning causal Bayesian network structures.

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Causal Inference in Hybrid Intervention Trials Involving Treatment Choice

包含治疗选择的混合干预试验中的因果推断

Randomized allocation of treatments is a cornerstone of experimental design but has drawbacks when a limited set of individuals are willing to be randomized, or the act of randomization undermines the success of the treatment. Choice-based experimental designs allow a subset of the participants to choose their treatments. We discuss here causal inferences for hybrid experimental designs in which some participants are randomly allocated to treatments and others receive their…

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Estimating Incumbency Advantage and Its Variation, as an Example of a Before–After Study

任职优势及其差异的估计:一项前后比较研究

Incumbency advantage is one of the most widely studied features in American legislative elections. In this article we construct and implement an estimate that allows incumbency advantage to vary between individual incumbents. This model predicts that open-seat elections will be less variable than those with incumbents running, an observed empirical pattern that is not explained by previous models. We apply our method to the U.S. House of Representatives in the twentieth…

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Statistical Treatment Choice

统计辅助的处理方案选择

Choosing among a number of treatments the most suitable for a particular client is an issue of everyday concern. In this article two methodological advances for statistically assisted treatment choice are developed: First, it permits one to combine a dataset on previously treated clients with a dataset on new clients when the regressors available in these two datasets do not coincide. It thereby incorporates additional regressors on previously treated clients that are not…

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Toward Causal Inference With Interference

存在个体间干扰时的因果推断

A fundamental assumption usually made in causal inference is that of no interference between individuals (or units); that is, the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in the dependent happenings of infectious diseases, whether one person becomes infected depends on who else in the population is vaccinated. In this…

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Statistical modeling of causal effects in continuous time

连续时间因果效应的统计建模

This article studies the estimation of the causal effect of a time-varying treatment on time-to-an-event or on some other continuously distributed outcome. The paper applies to the situation where treatment is repeatedly adapted to time-dependent patient characteristics. The treatment effect cannot be estimated by simply conditioning on these time-dependent patient characteristics, as they may themselves be indications of the treatment effect. This time-dependent confounding…

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