论文标题
平均治疗效果的半参数单索引估计
Semiparametric Single-Index Estimation for Average Treatment Effects
论文作者
论文摘要
我们提出了一种半参数方法，以在观察数据的假设下估计平均治疗效果。我们的估计方法通过估计通过Hermite多项式涉及的单点链路函数来减轻倾向得分函数的错误指定问题。我们的方法在计算上是可以进行的，并且允许适度的尺寸协变量。我们提供估计器的较大样本特性并显示其有效性。同样，平均治疗效应估计器达到了参数速率和渐近正态性。我们广泛的蒙特卡洛研究表明，所提出的估计量在有限样品中是有效的。将我们的方法应用于孕妇吸烟和婴儿健康，我们发现，由于倾向得分错误，对吸烟对出生体重的影响的常规估计可能会产生偏差，并且我们对职业培训计划的分析揭示了比以前的工作更精确估计的收益效应。这些应用程序表明，解决模型错误指定如何实质上影响我们对关键政策相关治疗效果的理解。
We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the single-index link function involved through Hermite polynomials. Our approach is computationally tractable and allows for moderately large dimension covariates. We provide the large sample properties of the estimator and show its validity. Also, the average treatment effect estimator achieves the parametric rate and asymptotic normality. Our extensive Monte Carlo study shows that the proposed estimator is valid in finite samples. Applying our method to maternal smoking and infant health, we find that conventional estimates of smoking's impact on birth weight may be biased due to propensity score misspecification, and our analysis of job training programs reveals earnings effects that are more precisely estimated than in prior work. These applications demonstrate how addressing model misspecification can substantively affect our understanding of key policy-relevant treatment effects.
