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SUMMARY This study considers the practically important case of nonparametrically estimating heterogeneous average treatment effects that vary with a limited number of discrete and continuous covariates in a selection-on-observables framework where the number of possible confounders is very large. We propose a two-step estimator for which the first step is estimated by machine learning. We show that this estimator has desirable statistical properties such as consistency,…

调节效应的因果机器学习AI 解读

It is valuable for any decision maker to know the impact of decisions (treatments) on average and for subgroups.The causal machine learning literature has recently provided tools for estimating group average treatment effects (GATE) to better describe treatment heterogeneity.This article addresses the challenge of interpreting such differences in treatment effects between groups while accounting for variations in other covariates.We propose a new parameter, the balanced…

Summary We investigate the finite-sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an empirical Monte Carlo study that relies on arguably realistic data generation processes (DGPs) based on actual data in an observational setting. We consider 24 DGPs, eleven causal machine learning estimators, and three aggregation levels of the estimated effects. Four of the considered estimators perform…

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…

倾向得分匹配与加权估计量推断方法的有限样本表现AI 解读

This article investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyze both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation designs, which are based on German register data and U.S. survey data. We vary the design w.r.t. treatment selectivity,…

序贯条件独立下中介效应估计量的有限样本表现AI 解读

Using a comprehensive simulation study based on empirical data, this article investigates the finite sample properties of different classes of parametric and semiparametric estimators of (natural) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers…

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…

基于倾向得分的估计量表现AI 解读

We investigate the finite sample properties of a large number of estimators for the average treatment effect on the treated that are suitable when adjustment for observed covariates is required, like inverse probability weighting, kernel and other variants of matching, as well as different parametric models. The simulation design used is based on real data usually employed for the evaluation of labour market programmes in Germany. We vary several dimensions of the design…

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…

劳动力市场项目评价中的序贯因果模型AI 解读

This article reviews inverse selection probability weighting to estimate dynamic causal effects. A distribution theory based on sequential generalized method of moments estimation is proposed and the method is applied to a reevaluation of some parts of the Swiss active labor market policy to obtain new results and discuss several issues about the implementation of the estimation procedure.

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面板数据 Probit 模型的若干设定检验AI 解读

Generalized score and Wald tests are proposed to test certain aspects of the specification of panel probit models. The procedures used to detect misspecifications, such as omitted and superfluous variables, heteroscedasticity, nonnormality, and random coefficient variations, are designed specifically for the case when the model is estimated by sequential methods. The finite sample distributions of the test statistics in correctly and incorrectly specified models are…

二元选择模型的半非参数估计及其劳动参与应用AI 解读

The authors adapt the estimation method proposed by A. R. Gallant and D. N. Nychka (1987) to binary-choice models. They present Monte Carlo and asymptotic comparisons with the probit estimator and discuss optimization algorithms, choice of starting values, and strategies f or choosing the number of parameters used in approximating the density. Seminonparametric estimation is almost as efficient as probit estimation in normal samples and performs better in nonnormal sample s.…