论文检索

弱可分模型中的内生虚拟变量AI 解读

In this paper, we consider the nonparametric identification and estimation of the average effect of a dummy endogenous regressor in models where the regressors are weakly but not additively separable from the error term. The model is not required to be strictly increasing in the error term, and the class of models considered includes limited dependent variable models such as discrete choice models. Conditions are established conditions under which it is possible to identify…

失业与流动性约束AI 解读

Abstract We present a dynamic framework for the interaction between borrowing (liquidity) constraints and deviations of actual hours from desired hours, both measured by discrete‐valued indicators, and estimate it as a system of dynamic binary and ordered probit models with panel data from the Panel Study of Income Dynamics. We analyze a household's propensity to be liquidity constrained by means of a dynamic binary probit model. We analyze qualitative aspects of the…

强处理选择下匹配算法的表现评估AI 解读

Abstract This paper investigates the method of matching regarding two crucial implementation choices: the distance measure and the type of algorithm. We implement optimal full matching—a fully efficient algorithm—and present a framework for statistical inference. The implementation uses data from the NLSY79 to study the effect of college education on earnings. We find that decisions regarding the matching algorithm depend on the structure of the data: In the case of strong…

卵巢癌治疗观察性研究中的精细平衡最小距离匹配AI 解读

In observational studies of treatment effects, matched samples have traditionally been constructed using two tools, namely close matches on one or two key covariates and close matches on the propensity score to stochastically balance large numbers of covariates. Here we propose a third tool, fine balance, obtained using the assignment algorithm in a new way. We use all three tools to construct a matched sample for an ongoing study of provider specialty in the treatment of…

随机实验中的单位间干扰AI 解读

In a randomized experiment comparing two treatments, there is interference between units if applying the treatment to one unit may affect other units. Interference implies that treatment effects are not comparisons of two potential responses that a unit may exhibit, one under treatment and the other under control, but instead are inherently more complex. Interference is common in social settings where people communicate, compete, or spread disease; in studies that treat one…

存在误分类时平均处理效应的估计AI 解读

This paper considers identification and estimation of the effect of a mismeasured binary regressor in a nonparametric or semiparametric regression, or the conditional average effect of a binary treatment or policy on some outcome where treatment may be misclassified. Failure to account for misclassification is shown to result in attenuation bias in the estimated treatment effect. An identifying assumption that overcomes this bias is the existence of an instrument for the…

考虑有效持续时间变化的重复测量治疗效应估计AI 解读

To assess treatment efficacy in clinical trials, certain clinical outcomes are repeatedly measured over time for the same subject. The difference in their means may characterize a treatment effect. Since treatment effectiveness lag and saturation times may exist, erosion of treatment effect often occurs during the observation period. Instead of using models based on ad hoc parametric or purely nonparametric time-varying coefficients, we model the treatment effectiveness…

处理或响应变量不可观测时因果效应的图形可识别性判据AI 解读

We consider the problem of using data in studies with an unobserved treatment/response variable in order to evaluate average causal effects, when cause-effect relationships between variables can be described by a directed acyclic graph and the corresponding recursive factorization of a joint distribution. The paper proposes graphical criteria to test whether average causal effects are identifiable even if a treatment/response variable is unobserved. If the answer is…

缺失响应问题中的经验似然推断及其观察性研究应用AI 解读

Summary The problem of missing response data is ubiquitous in medical and social science studies. In the case of responses that are missing at random (depending on some covariate information), analyses focused only on the complete data may lead to biased results. Various debias methods have been extensively studied in the literature, particularly the weighting method that was motivated by Horvitz and Thompson's estimators. To improve efficiency, Robins, Rotnitzky and Zhao…

摘要暂缺

摘要暂缺

高出生率会阻碍经济增长吗？AI 解读

This paper examines the impact of the birth rate on economic growth by using a panel data set of 28 provinces in China over twenty years. Because China's one-child policy applied only to the Han Chinese but not to minorities, this unique affirmative policy allows us to use the proportion of minorities in a province as an instrumental variable to identify the causal effect of the birth rate on economic growth. We find that the birth rate has a negative impact on economic…

抗击疟疾：等待“神奇”疫苗时应预防战争AI 解读

The World Health Organization estimates that 300 million clinical cases of malaria occur annually and observed that during the 80s and part of the 90s its incidence increased. In this paper, we explore the influence of refugees from civil wars on the incidence of malaria in the refugee-receiving countries. Using civil wars as an instrumental variable, we show that for each 1,000 refugees there are between 2,000 and 2,700 cases of malaria in the refugee-receiving country. On…

不完整数据与多种处理下的最小最大遗憾决策AI 解读

This note adds to the recent research project on treatment choice under ambiguity. I generalize the Manski (Journal of Econometrics, in press) analysis of minimax regret treatment choice by considering a more general setting and, more importantly, by solving for the treatment rule given finitely many (as opposed to two) treatments. The most interesting finding is that with three or more undominated treatments, the minimax regret treatment rule may assign the same treatment…

分位数处理效应的有效半参数估计AI 解读

This paper develops estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics. Identification is achieved without requiring computation of the conditional quantiles of the potential outcomes. Instead, the identification results for the marginal quantiles lead to an estimation procedure for the quantile treatment effect parameters that has two steps: nonparametric estimation of the propensity…

邻里效应的实验分析AI 解读

Families, primarily female-headed minority households with children, living in high-poverty public housing projects in five U.S. cities were offered housing vouchers by lottery in the Moving to Opportunity program. Four to seven years after random assignment, families offered vouchers lived in safer neighborhoods that had lower poverty rates than those of the control group not offered vouchers. We find no significant overall effects of this intervention on adult economic…

住房流动随机研究究竟说明了什么？AI 解读

During the past 20 years, social scientists using observational studies have generated a large and inconclusive literature on neighborhood effects. Recent workers have argued that estimates of neighborhood effects based on randomized studies of housing mobility, such as the “Moving to Opportunity” (MTO) demonstration, are more credible. These estimates are based on the implicit assumption of no interference between units; that is, a subject's value on the response depends…

利用倾向得分进行因果推断的分布方法AI 解读

Drawing inferences about the effects of treatments and actions is a common challenge in economics, epidemiology, and other fields. We adopt Rubin's potential outcomes framework for causal inference and propose two methods serving complementary purposes. One can be used to estimate average causal effects, assuming no confounding given measured covariates. The other can be used to assess how the estimates might change under various departures from no confounding. Both methods…

利用工具变量进行因果推断的回归与加权方法AI 解读

Please see the supplementary material for a correction and a new result.Recent researches in econometrics and statistics have gained considerable insights into the use of instrumental variables (IVs) for causal inference. A basic idea is that IVs serve as an experimental handle, the turning of which may change each individual's treatment status and, through and only through this effect, also change observed outcome. The average difference in observed outcome relative to that…

短面板中的代际流动与样本选择AI 解读

Abstract Using data from the first 11 waves of the BHPS, this paper measures the extent of the selection bias induced by standard coresidence conditions—bias that is expected to be severe in short panels—on measures of intergenerational mobility in occupational prestige. We try to limit the impact of other selection biases, such as those induced by labour market restrictions that are typically imposed in intergenerational mobility studies, by using different measures of…

存在不依从的三臂试验中的因果效应界AI 解读

Summary The paper considers the analysis of three-arm randomized trials with non-compliance. In these trials, the average causal effects of treatments within principal strata of compliance behaviour are of interest for better understanding the effect of the treatment. Unfortunately, even with the usual assumptions, the average causal effects of treatments within principal strata are not point identified. However, the observable data do provide useful information on the…

母职的工资成本：匹配估计分析AI 解读

Abstract We estimate the effect of motherhood on wages using matching. We distinguish between net and direct effects. The net effect includes the total wage costs, whereas the direct represents the causal effect. Since covariates are likely affected by motherhood, the latter effect is not immediately uncovered. We therefore implement two strategies: first, we confine the analysis to consider sector‐specific treatment effects; second, we impose additive separability on the…

私人保险、自选择与医疗服务使用AI 解读

Observed differences in medical utilization between the privately insured and uninsured reflect the combined effects of self-selection and insurance incentives (moral hazard). This article provides a Bayesian framework for decomposing the disparity into incentive and selection components. The effect of self-selection in private insurance on the number of doctor visits is estimated using a multiyear sample of the U.S. adult non-Medicare population obtained from the Medical…

存在选择偏差时总体协方差矩阵的恢复AI 解读

This paper considers the problem of using observational data in the presence of selection bias to identify causal effects in the framework of linear structural equation models. We propose a criterion for testing whether or not observed statistical dependencies among variables are generated by conditioning on a common response variable. When the answer is affirmative, we further provide formulations for recovering the covariance matrix of the whole population from that of the…