论文检索

处理评估中的内生性与无应答偏差：借助工具变量非参数识别因果效应AI 解读

Summary This paper proposes a nonparametric method for evaluating treatment effects in the presence of both treatment endogeneity and attrition/non‐response bias, based on two instrumental variables. Using a discrete instrument for the treatment and an instrument with rich (in general continuous) support for non‐response/attrition, we identify the average treatment effect on compliers as well as the total population under the assumption of additive separability of observed…

在断点回归设计中纳入协变量AI 解读

This article proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one-dimensional nonparametric regression, irrespective of the dimension of the continuously distributed elements in the conditioning set.…

直接与间接处理效应：工具变量下的因果链与中介分析AI 解读

Summary The paper discusses the non-parametric identification of causal direct and indirect effects of a binary treatment based on instrumental variables. We identify the indirect effect, which operates through a mediator (i.e. intermediate variable) that is situated on the causal path between the treatment and the outcome, as well as the unmediated direct effect of the treatment by using distinct instruments for the endogenous treatment and the endogenous mediator. We…

结合匹配与非参数工具变量估计：理论及积极劳动力市场政策评估AI 解读

Summary We show how instrumental variable and matching estimators can be combined in order to identify a broader array of treatment effects. Instrumental variable (IV) estimators are known to estimate effects only for the compliers, representing a subset of the entire population. By combining IV with matching, we can estimate the treatment effects for the always‐ and never‐takers as well. Since in many cases these groups are the (endogenous) outcome of some assignment…

内生性与样本流失下的多期处理效应评估AI 解读

This article develops a nonparametric methodology for treatment evaluation with multiple outcome periods under treatment endogeneity and missing outcomes. We use instrumental variables, pretreatment characteristics, and short-term (or intermediate) outcomes to identify the average treatment effect on the outcomes of compliers (the subpopulation whose treatment reacts on the instrument) in multiple periods based on inverse probability weighting. Treatment selection and…

内生性下的无条件分位数处理效应AI 解读

This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the binary treatment variable. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. We propose a weighting estimator that is extremely simple to implement. This estimator is root n consistent,…

断点回归设计中的分位数处理效应AI 解读

We introduce a nonparametric estimator for local quantile treatment effects in the regression discontinuity (RD) design. The procedure uses local distribution regression to estimate the marginal distributions of the potential outcomes. We illustrate the procedure through Monte Carlo simulations and an application on the distributional effects of a universal pre-K program in Oklahoma. We find that participation in a pre-K program significantly raises the lower end and the…

利用地区干预强度评估劳动力市场政策AI 解读

We estimate the effects of active labor market policies (ALMPs) on subsequent employment and earnings by nonparametric instrumental variable estimators. Very informative administrative Swiss data with detailed regional information are combined with exogenous regional variation in program participation probabilities to generate an instrument within well-defined local labor markets. We find that implementation of an ALMP increased individual employment probabilities by about…

统计辅助的处理方案选择AI 解读

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…

无需条件独立假设的倾向得分匹配：英国性别工资差距应用AI 解读

Propensity score matching is frequently used for estimating average treatment effects. Its applicability, however, is not confined to treatment evaluation. In this paper, it is shown that propensity score matching does not hinge on a selection on observables assumption and can be used to estimate not only adjusted means but also their distributions, even with non-i.i.d. sampling. Propensity score matching is used to analyze the gender wage gap among graduates in the UK. It…

摘要暂缺

二元因变量的非参数回归AI 解读

Finite-sample properties of non-parametric regression for binary dependent variables are analyzed. Non parametric regression is generally considered as highly variable in small samples when the number of regressors is large. In binary choice models, however, it may be more reliable since its variance is bounded. The precision in estimating conditional means as well as marginal effects is investigated in settings with many explanatory variables (14 regressors) and small…