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重新考察贸易创造与转移：模型不确定性和自然贸易伙伴效应AI 解读

SUMMARY The effect of preferential trade agreements (PTAs) on trade flows is subject to model uncertainty stemming from the diverse and even contradictory effects suggested by the theoretical PTA literature. The existing empirical literature has produced remarkably disparate results and the wide variety of empirical approaches reflects the uncertainty about the ‘correct’ set of explanatory variables that ought to be included in the analysis. To account for the model…

平稳时间序列的协方差矩阵估计AI 解读

We obtain a sharp convergence rate for banded covariance matrix estimates of stationary processes. A precise order of magnitude is derived for spectral radius of sample covariance matrices. We also consider a thresholded covariance matrix estimator that can better characterize sparsity if the true covariance matrix is sparse. As our main tool, we implement Toeplitz [Math. Ann. 70 (1911) 351–376] idea and relate eigenvalues of covariance matrices to the spectral densities or…

存在潜在变量与选择变量时的高维有向无环图学习AI 解读

We consider the problem of learning causal information between random variables in directed acyclic graphs (DAGs) when allowing arbitrarily many latent and selection variables. The FCI (Fast Causal Inference) algorithm has been explicitly designed to infer conditional independence and causal information in such settings. However, FCI is computationally infeasible for large graphs. We therefore propose the new RFCI algorithm, which is much faster than FCI. In some situations…

基于条件随机化检验的序贯监测AI 解读

Sequential monitoring in clinical trials is often employed to allow for early stopping and other interim decisions, while maintaining the type I error rate. However, sequential monitoring is typically described only in the context of a population model. We describe a computational method to implement sequential monitoring in a randomization-based context. In particular, we discuss a new technique for the computation of approximate conditional tests following restricted…

不可分结构系统中的局部间接最小二乘与平均边际效应AI 解读

We study the scope of local indirect least squares (LILS) methods for nonparametrically estimating average marginal effects of an endogenous cause X on a response Y in triangular structural systems that need not exhibit linearity, separability, or monotonicity in scalar unobservables. One main finding is negative: in the fully nonseparable case, LILS methods cannot recover the average marginal effect. LILS methods can nevertheless test the hypothesis of no effect in the…

重新审视工具变量的有效性AI 解读

This paper shows how valid inferences can be made when an instrumental variable does not perfectly satisfy the orthogonality condition. When there is a mild violation of the orthogonality condition, the Anderson and Rubin (1949) test is oversized. In order to correct this problem, the fractionally resampled Anderson–Rubin test is derived by modifying Wu’s (1990) resampling technique. We select half of the sample when resampling and obtain valid but conservative critical…

存在社会互动时处理响应的识别AI 解读

This paper develops a formal language for study of treatment response with social interactions, and uses it to obtain new findings on identification of potential outcome distributions. Defining a person's treatment response to be a function of the entire vector of treatments received by the population, I study identification when shape restrictions and distributional assumptions are placed on response functions. An early key result is that the traditional assumption of…

基于非对称极小极大遗憾准则的统计处理选择AI 解读

This paper studies the problem of treatment choice between a status quo treatment with a known outcome distribution and an innovation whose outcomes are observed only in a finite sample. I evaluate statistical decision rules, which are functions that map sample outcomes into the planner’s treatment choice for the population, based on regret, which is the expected welfare loss due to assigning inferior treatments. I extend previous work started by Manski (2004) that applied…

含协变量或实验有效性受限时的最小最大遗憾处理选择AI 解读

This paper continues the investigation of minimax regret treatment choice initiated by Manski (2004). Consider a decision maker who must assign treatment to future subjects after observing outcomes experienced in a sample. A certain scoring rule is known to achieve minimax regret in simple versions of this decision problem. I investigate its sensitivity to perturbations of the decision environment in realistic directions. They are as follows. (i) Treatment outcomes may be…

全国学校午餐计划对儿童健康的影响：非参数界限分析AI 解读

Children in households reporting the receipt of free or reduced-price school meals through the National School Lunch Program (NSLP) are more likely to have negative health outcomes than observationally similar nonparticipants. Assessing causal effects of the program is made difficult, however, by missing counterfactuals and systematic underreporting of program participation. Combining survey data with auxiliary administrative information on the size of the NSLP caseload, we…

激励之外：学校是否有效使用问责奖励？AI 解读

We use a regression discontinuity design to analyze an understudied aspect of school accountability systems—how schools use financial rewards. For two years, California's accountability system financially rewarded schools based on a deterministic function of test scores. Qualifying schools received per-pupil awards amounting to about 1% of statewide per-pupil spending. Corroborating anecdotal evidence that awards were paid out as teacher bonuses, we find no evidence that…

利用异方差识别与估计含误测及内生回归变量的模型AI 解读

This article proposes a new method of obtaining identification in mismeasured regressor models, triangular systems, and simultaneous equation systems. The method may be used in applications where other sources of identification, such as instrumental variables or repeated measurements, are not available. Associated estimators take the form of two-stage least squares or generalized method of moments. Identification comes from a heteroscedastic covariance restriction that is…

排除限制不确定时的工具变量回归：贝叶斯方法AI 解读

SUMMARY The identification of structural parameters in the linear instrumental variables (IV) model is typically achieved by imposing the prior identifying assumption that the error term in the structural equation of interest is orthogonal to the instruments. Since this exclusion restriction is fundamentally untestable, there are often legitimate doubts about the extent to which the exclusion restriction holds. In this paper I illustrate the effects of such prior uncertainty…

最优工具变量的稀疏模型与方法：征收权应用AI 解读

We develop results for the use of LASSO and Post-LASSO methods to form firststage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p, that apply even when p is much larger than the sample size, n.We rigorously develop asymptotic distribution and inference theory for the resulting IV estimators and provide conditions under which these estimators are asymptotically oracle-efficient.In simulation experiments, the…

聚类无应答下的逆概率加权AI 解读

Correlated nonresponse within clusters arises in certain survey settings. It is often represented by a random effects model and assumed to be cluster-specific nonignorable, in the sense that survey and nonresponse outcomes are conditionally independent given cluster-level random effects. Two basic forms of inverse probability weights are considered: response propensity weights based on a marginal model, and weights based on predicted random effects. It is shown that both…

平均处理效应非参数估计中的协变量选择AI 解读

Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give…

二元工具变量模型的似然分析AI 解读

Instrumental variables are widely used for the identification of the causal effect of one random variable on another under unobserved confounding. The distribution of the observable variables for a discrete instrumental variable model satisfies certain inequalities but no conditional independence relations. Such models are usually tested by checking whether the relative frequency estimators of the parameters satisfy the constraints. This ignores sampling uncertainty in the…

死亡截断结局下主层化因果效应的可识别性与估计AI 解读

In medical studies, there are many situations where the final outcomes are truncated by death, in which patients die before outcomes of interest are measured. In this article we consider identifiability and estimation of causal effects by principal stratification when some outcomes are truncated by death. Previous studies mostly focused on large sample bounds, Bayesian analysis, sensitivity analysis. In this article, we propose a new method for identifying the causal…

因果推断中连续中间变量的贝叶斯半参数建模AI 解读

In causal inference studies, treatment comparisons often need to be adjusted for confounded post-treatment variables. Principal stratification (PS) is a framework to deal with such variables within the potential outcome approach to causal inference. Continuous intermediate variables introduce inferential challenges to PS analysis. Existing methods either dichotomize the intermediate variable, or assume a fully parametric model for the joint distribution of the potential…

多区域采样及其在贝叶斯网络结构推断中的应用AI 解读

When a posterior distribution has multiple modes, unconditional expectations, such as the posterior mean, may not offer informative summaries of the distribution. Motivated by this problem, we propose to decompose the sample space of a multimodal distribution into domains of attraction of local modes. Domain-based representations are defined to summarize the probability masses of and conditional expectations on domains of attraction, which are much more informative than the…

用于评估主层直接效应的增强实验设计AI 解读

Summary Many studies involving causal questions are often concerned with understanding the causal pathways by which a treatment affects an outcome. Thus, the concept of ‘direct’versus‘indirect’ effects comes into play. We tackle the problem of disentangling direct and indirect effects by investigating new augmented experimental designs, where the treatment is randomized, and the mediating variable is not forced, but only randomly encouraged. There are two key features of our…

使用Aalen加性风险模型估计生存数据的直接效应AI 解读

Summary We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned and when the association between the intermediate variable and the survival outcome is confounded only by measured factors, which may themselves be affected by the exposure.…

处理效应估计的因果图及其在有效协变量选择中的应用AI 解读

Careful examination of the structure determining treatment choice and outcomes, as advocated by Heckman (2008), is central to the design of treatment effect estimators and, in particular, proper choice of covariates. Here, we demonstrate how causal diagrams developed in the machine learning literature by Judea Pearl and his colleagues, but not so well known to economists, can play a key role in this examination by using these methods to give a detailed analysis of the choice…

走上毒品阶梯：开始使用烈性毒品的贝叶斯分析AI 解读

Abstract Since empirical studies have shown that cannabis users are much more likely to initiate hard drug use, a causal linkage has been suggested (‘gateway hypothesis’). However, individual differences in proneness and accessibility to drugs provide alternative non‐causal explanations for the observed drug use pattern. We propose a Bayesian estimation and predictive framework to analyze the effects and relative importance of previous cannabis use, proneness and…