Simple least squares estimator for treatment effects using propensity score residuals
Simple least squares estimator for treatment effects using propensity score residuals
复制标题
使用倾向得分残差的治疗效果的简单最小二乘估计器
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Myoung‐jae Lee
中科院分区：
文献类型：
--
作者：
Myoung‐jae Lee
摘要倾向分数匹配在分析非随机化二元处理的效果时被广泛用于控制协变量。然而，它需要几个武断的决定，比如使用多少匹配的主题以及如何选择它们。本文提出了一种简单的最小二乘估计器，用倾向得分残差代替处理，可能还有响应变量。如果倾向得分函数被正确地指定，则所提出的估计量以半参数方式控制协变量。此外，与匹配、回归估计、加权和双重稳健估计等备选方法相比，它在数值上是稳定的，并且相对容易使用。所提出的估计量还有一个简单有效的渐近方差估计量，它在小样本下工作得很好。将最小二乘估计推广到多个处理和非连续分布的响应。仿真研究表明，它比竞争对手具有更低的均方误差。
Summary Propensity score matching is widely used to control covariates when analysing the effects of a nonrandomized binary treatment. However, it requires several arbitrary decisions, such as how many matched subjects to use and how to choose them. In this paper a simple least squares estimator is proposed, where the treatment, and possibly the response variable, is replaced by the propensity score residual. The proposed estimator controls covariates semiparametrically if the propensity score function is correctly specified. Furthermore, it is numerically stable and relatively easy to use, compared with alternatives such as matching, regression imputation, weighting, and doubly robust estimators. The proposed estimator also has a simple valid asymptotic variance estimator that works well in small samples. The least squares estimator is extended to multiple treatments and noncontinuously distributed responses. A simulation study demonstrates that it has lower mean squared error than its competitors.