摘要翻译:
Wang和Tchetgen Tchetgen(2017)研究了当某些混杂物未测量时,平均治疗效果的识别和估计。在辨识条件下,证明了半参数有效影响函数依赖于五个未知函数。他们提出将所有功能参数化,通过用估计功能替换未知功能,从有效影响函数中估计平均治疗效果。当某些函数被正确指定时,他们的估计量是一致的;当所有函数被正确指定时,他们的估计量达到半参数效率界。在应用程序中,这些功能很可能都被错误指定。因此,它们的估计量可能不一致或一致,但不是有效的。本文提出了一种不需要任何函数参数化的替代估计量。我们证明了所提出的估计量总是相合的,并且总是达到半参数效率界。给出了一种简单直观的渐近方差估计方法,并通过小规模仿真研究表明,在有限样本条件下,所提出的估计方法优于已有的方法。
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英文标题:
《A Simple and Efficient Estimation of the Average Treatment Effect in the
Presence of Unmeasured Confounders》
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作者:
Chunrong Ai, Lukang Huang, and Zheng Zhang
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最新提交年份:
2018
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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英文摘要:
Wang and Tchetgen Tchetgen (2017) studied identification and estimation of the average treatment effect when some confounders are unmeasured. Under their identification condition, they showed that the semiparametric efficient influence function depends on five unknown functionals. They proposed to parameterize all functionals and estimate the average treatment effect from the efficient influence function by replacing the unknown functionals with estimated functionals. They established that their estimator is consistent when certain functionals are correctly specified and attains the semiparametric efficiency bound when all functionals are correctly specified. In applications, it is likely that those functionals could all be misspecified. Consequently their estimator could be inconsistent or consistent but not efficient. This paper presents an alternative estimator that does not require parameterization of any of the functionals. We establish that the proposed estimator is always consistent and always attains the semiparametric efficiency bound. A simple and intuitive estimator of the asymptotic variance is presented, and a small scale simulation study reveals that the proposed estimation outperforms the existing alternatives in finite samples.
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PDF链接:
https://arxiv.org/pdf/1807.05678