论文标题
用筛子法对平均治疗效果的半参数估计
Semiparametric Estimation of Average Treatment Effect with Sieve Method
论文作者
论文摘要
由于必须正确指定结果模型或治疗分配模型，因此在观察性研究中正确识别治疗效果非常困难。在本文中，我们使用半参数模型的优点，我们使用单索引模型来建立结果模型和治疗分配模型，这可以使链接功能无限制并具有无限的支持。链接函数被认为是无限维函数空间中的一个点，我们可以同时估算链接函数和索引参数。筛子方法用于近似链接函数，并通过简单的线性回归获得平均处理效果的估计量。我们建立了所提出的估计量的渐近特性。通过模拟研究和经验示例评估了所提出的估计量的有限样本性能。
Correctly identifying treatment effects in observational studies is very difficult due to the fact that the outcome model or the treatment assignment model must be correctly specified. Taking advantages of semiparametric models in this article, we use single-index models to establish the outcome model and the treatment assignment model, which can allow the link function to be unbounded and have unbounded support. The link function is regarded as a point in an infinitely dimensional function space, and we can estimate the link function and the index parameter simultaneously. The sieve method is used to approximate the link function and obtain the estimator of the average treatment effect by the simple linear regression. We establish the asymptotic properties of the proposed estimator. The finite-sample performance of the proposed estimator is evaluated through simulation studies and an empirical example.
