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裂区实验中设计式与模型式因果推断的协调AI 解读

The split-plot design arose from agricultural science with experimental units, also known as the subplots, nested within groups known as the whole plots. It assigns different interventions at the whole-plot and subplot levels, respectively, providing a convenient way to accommodate hard-to-change factors. By design, subplots within the same whole plot receive the same level of the whole-plot intervention, and thereby induce a group structure on the final treatment…

一般自标准化和的精细 Cramér 型中偏差定理及相关变量与缩尾均值应用AI 解读

Let {(Xi,Yi)}i=1n be a sequence of independent bivariate random vectors. In this paper, we establish a refined Cramér-type moderate deviation theorem for the general self-normalized sum ∑ i=1nXi/(∑i=1nYi2)1/2, which unifies and extends the classical Cramér (Actual. Sci. Ind. 736 (1938) 5–23) theorem and the self-normalized Cramér-type moderate deviation theorems by Jing, Shao and Wang (Ann. Probab. 31 (2003) 2167–2215) as well as the further refined version by Wang (J.…

职业培训的动态处理效应AI 解读

Summary This paper estimates the dynamic returns to job training. We posit a model of sequential training participation, where decisions and outcomes depend on observed and unobserved characteristics. We analyze different treatment effects, including policy relevant parameters, and link them to continuation values and latent skills. The empirical analysis exploits administrative data combining job training records, matched employee‐employer information, and pre‐labor market…

求职援助项目的动态评估AI 解读

Summary This paper evaluates a job search assistance program for unemployed teachers where the assignment to the program is dynamic. We discuss the methodology of estimating dynamic treatment effects and identification conditions. In the empirical analysis, we use administrative data from a unique institutional environment in which we observe all variables determining assignment to the job search assistance program. This allows us to compare results from a dynamic…

含隐变量的因果图模型中的高效调整集AI 解读

Summary We study the selection of adjustment sets for estimating the interventional mean under a point exposure dynamic treatment regime, that is, a treatment rule that depends on the subject’s covariates. We assume a nonparametric causal graphical model with, possibly, hidden variables and at least one adjustment set comprised of observable variables. We provide the definition of a valid adjustment set for a point exposure dynamic treatment regime, which generalizes the…

多重处理与二元结果下因果效应的可识别性AI 解读

Summary Unobserved confounding presents a major threat to causal inference in observational studies. Recently, several authors have suggested that this problem could be overcome in a shared confounding setting where multiple treatments are independent given a common latent confounder. It has been shown that under a linear Gaussian model for the treatments, the causal effect is not identifiable without parametric assumptions on the outcome model. In this note, we show that…

最小化及其他协变量自适应随机化下的平均处理效应推断AI 解读

Summary Covariate-adaptive randomization schemes such as minimization and stratified permuted blocks are often applied in clinical trials to balance treatment assignments across prognostic factors. The existing theory for inference after covariate-adaptive randomization is mostly limited to situations where a correct model between the response and covariates can be specified or the randomization method has well-understood properties. Based on stratification with covariate…

一般干扰下因果效应随机化检验的图论方法AI 解读

Abstract Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighbouring streets. Classical randomization tests typically break down in this setting because many null hypotheses of interest are no longer sharp under interference. A promising alternative is to instead construct a conditional randomization test on a subset of units and assignments for which a…

利用分布不连续协变量检验基于可观测变量的选择假设AI 解读

We present a test of the selection on observables assumption that neither requires instruments nor excluded covariates from the structural function. Instead, we rely on the presence of a discontinuously distributed variable among the set of controls. We develop formal testing procedures for a non-parametric additively separable model for binary and finite treatment variables. We also outline a nonparametric nonseparable extension. Our test is easy to implement and should be…

气候政策的实体效应：融资约束与溢出AI 解读

We document that localized policies aimed at mitigating climate risk can have unintended consequences due to regulatory arbitrage by firms. Using a difference-in-differences framework to study the impact of the California cap-and-trade program with U.S. plant-level data, we show that financially constrained firms shift emissions and output from California to other states where they have similar plants that are underutilized. By contrast, unconstrained firms do not make such…

注意力触发与投资者风险承担AI 解读

This paper investigates how individual attention triggers influence financial risk-taking based on a large sample of trading records from a brokerage service that sends standardized push messages on stocks to retail investors. By exploiting the data in a difference-in-differences (DID) setting, we find attention triggers increase investors’ risk-taking. Our DID coefficient implies attention trades carry, on average, a 19 percentage-point-higher leverage than non-attention…

准实验移位份额研究设计AI 解读

Abstract Many studies use shift-share (or “Bartik”) instruments, which average a set of shocks with exposure share weights. We provide a new econometric framework for shift-share instrumental variable (SSIV) regressions in which identification follows from the quasi-random assignment of shocks, while exposure shares are allowed to be endogenous. The framework is motivated by an equivalence result: the orthogonality between a shift-share instrument and an unobserved residual…

高维中介假设的多重检验程序AI 解读

Mediation analysis is of rising interest in epidemiology and clinical trials. Among existing methods, the joint significance (JS) test yields an overly conservative type I error rate and low power, particularly for high-dimensional mediation hypotheses. In this article we develop a multiple-testing procedure that accurately controls the family-wise error rate (FWER) and the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. The core of our…

存在竞争事件时用于因果推断的可分离效应AI 解读

In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatment effect on the event of interest not mediated by its effect on the competing event. The separable indirect effect is the treatment effect on the event of interest only through its effect on the competing event. Similar…

基于变化中的变化方法的直接与间接效应AI 解读

We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify average and quantile direct and indirect effects for various subgroups under the…

高维数据下条件平均处理效应的估计AI 解读

Given the unconfoundedness assumption, we propose new nonparametric estimators for the reduced dimensional conditional average treatment effect (CATE) function. In the first stage, the nuisance functions necessary for identifying CATE are estimated by machine learning methods, allowing the number of covariates to be comparable to or larger than the sample size. The second stage consists of a low-dimensional local linear regression, reducing CATE to a function of the…

反事实处理效应：估计与推断AI 解读

This article proposes statistical methods to evaluate the quantile counterfactual treatment effect (QCTE) if one were to change the composition of the population targeted by a status quo program. QCTE enables a researcher to carry out an ex-ante assessment of the distributional impact of certain policy interventions or to investigate the possible explanations for treatment effect heterogeneity. Assuming unconfoundedness and invariance of the conditional distributions of the…

非平稳数据下的反事实分析与推断AI 解读

Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial/synthetic counterfactual from a pool of “untreated” peers, organized in a panel data structure. In this article, we investigate the consequences of…

“胡萝卜与大棒”政策的治疗效应与制度效应AI 解读

Public employment service (PES) agencies and caseworkers (CWs) often have substantial leeway in the design and implementation of active labor market policies for the unemployed, and they use policies to a varying extent. We estimate regime effects which capture how CW and PES affect outcomes through different policy intensities. These operate potentially on all forward-looking job seekers regardless of actual treatment exposure. We consider regime effects for two sets of…

面板数据模型中的反馈AI 解读

Much of the analysis of panel data has been based on an assumption of strict exogeneity. Distributions are specified for outcome variables conditional on a latent individual effect and conditional on observed predictor variables at all dates, with the future values of the predictor variables assumed to have no effect on the conditional distribution. The paper relaxes this assumption in order to allow for lagged dependent variables and, more generally, for feedback from…

平均线性回归函数的半参数有效估计AI 解读

Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, or non-mutually exclusive “treatments”. Consider the linear regression of Y onto X in a subpopulation homogeneous in W=w (formally a conditional linear predictor). Let b0w be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average β0=Eb0W.…

错位采用情形下双重差分的设计型分析AI 解读

In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the staggered adoption setting where units, e.g, individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment…

谁获益、谁受损？条件因果效应识别与工资变化的福利影响AI 解读

The incidence of tax and other policy changes depends on their impact on equilibrium wages. In a standard model of labor supply, the impact of wage changes on a worker’s welfare equals current labor supply times the induced wage change. Worker heterogeneity implies that wage changes vary across workers. In this context, in order to identify welfare effects one needs to identify the causal effect of policy changes on wages conditional on baseline labor supply and wages.

新冠疫情期间严厉封锁的经济影响：中国湖北的经验AI 解读

Summary This paper uses a panel data approach to assess the evolution of economic consequences of the drastic lockdown policy in the epicenter of COVID‐19—the Hubei Province of China during worldwide curbs on economic activity. We find that the drastic 76‐day COVID‐19 lockdown policy brought huge negative impacts on Hubei's economy. In 2020:q1, the lockdown quarter, the treatment effect on GDP was about 37% of the counterfactual. However, the drastic lockdown also brought…