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Causal inference in partially linear structural equation models

部分线性结构方程模型中的因果推断

We consider identifiability of partially linear additive structural equation models with Gaussian noise (PLSEMs) and estimation of distributionally equivalent models to a given PLSEM. Thereby, we also include robustness results for errors in the neighborhood of Gaussian distributions. Existing identifiability results in the framework of additive SEMs with Gaussian noise are limited to linear and nonlinear SEMs, which can be considered as special cases of PLSEMs with…

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ArCo: An artificial counterfactual approach for high-dimensional panel time-series data

ArCo:高维面板时间序列的人工反事实方法

We consider a new, flexible and easy-to-implement method to estimate thecausal effects of an intervention on a single treated unit when a control group is not available and which nests previous proposals in the literature. It is a two-step methodology where in the first stage, a counterfactual is estimated based on a large-dimensional set of variables from a pool of untreated units by means of shrinkage methods, such as the least absolute shrinkage and selection operator…

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Home away from home? Foreign demand and London house prices

异乡的家?外国需求与伦敦房价

Identifying the effects of “flights to safety” on asset prices using pure time-series methods is difficult because crises are infrequent. We develop a new cross-sectional identification approach, motivated by the insight that investors may differ in their “preferred habitats” within a broad asset class. We apply the method to the question of whether foreign capital is responsible for residential real estate price movements in global cities such as London and New York,…

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Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance

谁受益于普惠托育?早期托育参与的边际回报估计

We examine heterogeneous treatment effects of a universal child care program in Germany by exploiting variation in attendance caused by a reform that led to a large expansion staggered across municipalities. Drawing on novel administrative data from the full population of compulsory school entry examinations, we find that children with lower (observed and unobserved) gains are more likely to select into child care than children with higher gains. Children from disadvantaged…

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Potential Unemployment Insurance Duration and Labor Supply: The Individual and Market-Level Response to a Benefit Cut

失业保险潜在领取期限与劳动供给:福利削减的个体及市场反应

We examine how a 16-week cut in potential unemployment insurance (UI) duration in Missouri affected search behavior of UI recipients and the aggregate labor market. Using a regression discontinuity design (RDD), we estimate marginal effects of maximum duration on UI and nonemployment spells of 0.45 and 0.25, respectively. We simulate the unemployment rate implied by the RDD estimates assuming no market-level externalities. The implied response closely approximates the…

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Time Use and Labor Productivity: The Returns to Sleep

时间利用与劳动生产率:睡眠的回报

Abstract We investigate how the largest use of time—sleep—affects productivity. Time use data from the United States allow us to test a model in which sleep improves productivity. Consistent with theory, we find sleep is more complementary to home production than to leisure for nonemployed individuals. We then show that later sunset time reduces worker sleep and earnings. After ruling out alternative hypotheses, we implement an instrumental variables specification that…

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Robots at Work

工作中的机器人

We analyze for the first time the economic contributions of modern industrial robots, which are flexible, versatile, and autonomous machines. We use novel panel data on robot adoption within industries in seventeen countries from 1993 to 2007 and new instrumental variables that rely on robots’ comparative advantage in specific tasks. Our findings suggest that increased robot use contributed approximately 0.36 percentage points to annual labor productivity growth, while at…

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On Mitigating the Analytical Limitations of Finely Stratified Experiments

缓解精细分层实验的分析局限

Summary Although attractive from a theoretical perspective, finely stratified experiments such as paired designs suffer from certain analytical limitations that are not present in block-randomized experiments with multiple treated and control individuals in each block. In short, when using a weighted difference in means to estimate the sample average treatment effect, the traditional variance estimator in a paired experiment is conservative unless the pairwise average…

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How important are fixed effects and time trends in estimating returns to schooling? Evidence from a replication of Jacobson, Lalonde, and Sullivan, 2005

估计教育回报时固定效应与时间趋势有多重要?一项复现研究

Summary A substantial and rapidly growing literature has developed around estimating earnings gains from 2‐year college degrees using administrative data. These papers almost universally employ a person‐level fixed‐effects strategy to estimate earnings premia net of fixed attributes. We note that the seminal piece on which these papers build—Jacobson, Lalonde, & Sullivan, Journal of Econometrics , 2005, 125 (1–2), 271–304—provides theoretical and empirical evidence for the…

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Interpretable Dynamic Treatment Regimes

可解释的动态治疗方案

Precision medicine is currently a topic of great interest in clinical and intervention science. A key component of precision medicine is that it is evidence-based, i.e., data-driven, and consequently there has been tremendous interest in estimation of precision medicine strategies using observational or randomized study data. One way to formalize precision medicine is through a treatment regime, which is a sequence of decision rules, one per stage of clinical intervention,…

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Placebo Response as a Latent Characteristic: Application to Analysis of Sequential Parallel Comparison Design Studies

将安慰剂反应视为潜在特征:序贯平行比较设计研究的分析

In clinical trials, placebo response can affect the inference about efficacy of the studied treatment. It is important to have a robust way to classify trial subjects with respect to their response to placebo. Simple, criterion-based classification may lead to classification error and bias the inference. The uncertainty about placebo response characteristic has to be factored into the treatment effect estimation. We propose a novel approach that views the placebo response as…

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Inference Under Covariate-Adaptive Randomization

协变量自适应随机化下的推断

This article studies inference for the average treatment effect in randomized controlled trials with covariate-adaptive randomization. Here, by covariate-adaptive randomization, we mean randomization schemes that first stratify according to baseline covariates and then assign treatment status so as to achieve “balance” within each stratum. Our main requirement is that the randomization scheme assigns treatment status within each stratum so that the fraction of units being…

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Can Business Owners Form Accurate Counterfactuals? Eliciting Treatment and Control Beliefs About Their Outcomes in the Alternative Treatment Status

企业主能否形成准确反事实?征询处理组与对照组对另一处理状态下结果的信念

A survey of participants in a large-scale business plan competition experiment, in which winners received an average of U.S. $50,000 each, is used to elicit ex-post beliefs about what the outcomes would have been in the alternative treatment status. Participants are asked the percent chance they would be operating a firm, and the number of employees and monthly sales they would have, had their treatment status been reversed. The study finds the control group to have…

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Testing Conditional Mean Independence Under Symmetry

对称性条件下的条件均值独立检验

Conditional mean independence (CMI) is one of the most widely used assumptions in the treatment effect literature to achieve model identification. We propose a Kolmogorov–Smirnov-type statistic to test CMI under a specific symmetry condition. We also propose a bootstrap procedure to obtain the p-values and critical values that are required to carry out the test. Results from a simulation study suggest that our test can work very well even in small to moderately sized…

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Workers beneath the Floodgates: Low-Wage Import Competition and Workers’ Adjustment

洪闸之下的工人:低工资进口竞争与劳动者调整

Abstract Using employee-employer matched data, I analyze the impact of a low-wage trade shock on manufacturing workers in a high-wage country, Denmark, and how they adjust to the shock over a decade. I derive causal effects by exploiting the dismantling of the Multifiber Arrangement quotas on products from China upon its WTO accession as a quasi-natural experiment and use within-industry, within-occupation heterogeneity in workers’ exposure to this shock. I find significant…

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Randomization-based causal inference from split-plot designs

裂区设计中的随机化因果推断

Under the potential outcomes framework, we propose a randomization based estimation procedure for causal inference from split-plot designs, with special emphasis on $2^{2}$ designs that naturally arise in many social, behavioral and biomedical experiments. Point estimators of factorial effects are obtained and their sampling variances are derived in closed form as linear combinations of the between- and within-group covariances of the potential outcomes. Results are compared…

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Beyond plausibly exogenous

超越“近似外生”:工具变量排除限制的敏感性分析

We synthesize two recent advances in the literature on instrumental variable (IV) estimation that test and relax the exclusion restriction. Our approach first estimates the direct effect of the IV on the outcome in a subsample for which the IV does not affect the treatment variable. Subsequently, this estimate for the direct effect is used as input for the plausibly exogenous method developed by Conley, Hansen and Rossi. This two‐step procedure provides a novel and informed…

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Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods

依赖结构限制下仅有两期数据的双重差分分位数处理效应

This paper shows that the Conditional Quantile Treatment Effect on the Treated is identified under (i) a Conditional Distributional Difference in Differences assumption and (ii) a new assumption that the dependence (the copula) between the change in untreated potential outcomes and the initial level of untreated potential outcomes is the same for the treated group and untreated group. We consider estimation and inference with discrete covariates and propose a uniform…

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WEAK CONVERGENCE TO STOCHASTIC INTEGRALS UNDER PRIMITIVE CONDITIONS IN NONLINEAR ECONOMETRIC MODELS

非线性计量模型中原始条件下向随机积分的弱收敛

Limit theory with stochastic integrals plays a major role in time series econometrics. In earlier contributions on weak convergence to stochastic integrals, the literature commonly uses martingale and semi-martingale structures. Liang, Phillips, Wang, and Wang (2016) (see also Wang (2015), Chap. 4.5) currently extended weak convergence to stochastic integrals by allowing for a linear process or a α -mixing sequence in innovations. While these martingale, linear process and α…

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Identification of treatment effects with selective participation in a randomized trial

随机试验存在选择性参与时处理效应的识别

Randomized trials (RTs) are used to learn about treatment effects. This paper studies identification of average treatment response (ATR) and average treatment effect (ATE) from RT data under various assumptions. The focus is the problem of external validity of the RT. RT data need not point identify the ATR or ATE because of selective participation in the RT. The paper reports partial‐identification and point‐identification results for the ATR and ATE based on RT data under…

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Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes

事件时间结局下最优个体化治疗规则的目标学习集成

We consider estimation of an optimal individualized treatment rule when a high-dimensional vector of baseline variables is available. Our optimality criterion is with respect to delaying the expected time to occurrence of an event of interest. We use semiparametric efficiency theory to construct estimators with properties such as double robustness. We propose two estimators of the optimal rule, which arise from considering two loss functions aimed at directly estimating the…

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Covariate association eliminating weights: a unified weighting framework for causal effect estimation

消除协变量关联的权重:因果效应估计的统一加权框架

Weighting methods offer an approach to estimating causal treatment effects in observational studies. However, if weights are estimated by maximum likelihood, misspecification of the treatment assignment model can lead to weighted estimators with substantial bias and variance. In this paper, we propose a unified framework for constructing weights such that a set of measured pretreatment covariates is unassociated with treatment assignment after weighting. We derive conditions…

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Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects

异质个体化治疗效应下最优治疗决策的最大最小投影学习

Summary A salient feature of data from clinical trials and medical studies is inhomogeneity. Patients not only differ in baseline characteristics, but also in the way that they respond to treatment. Optimal individualized treatment regimes are developed to select effective treatments based on patient's heterogeneity. However, the optimal treatment regime might also vary for patients across different subgroups. We mainly consider patients’ heterogeneity caused by groupwise…

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Confidence Intervals for Causal Effects with Invalid Instruments by Using Two-Stage Hard Thresholding with Voting

存在无效工具变量时的因果效应置信区间:两阶段硬阈值与投票

Summary A major challenge in instrumental variable (IV) analysis is to find instruments that are valid, or have no direct effect on the outcome and are ignorable. Typically one is unsure whether all of the putative IVs are in fact valid. We propose a general inference procedure in the presence of invalid IVs, called two-stage hard thresholding with voting. The procedure uses two hard thresholding steps to select strong instruments and to generate candidate sets of valid IVs.…

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