摘要翻译：
本文提出了一种在处理分配不混淆的情况下，将二元、多值、连续以及离散与连续混合处理统一起来的加权优化框架。该框架以一般损失函数为基础，将平均值、分位数和非对称最小二乘因果效应作为特例进行处理。对于这个一般框架，我们首先导出了治疗因果效应的半参数效率界，将已有的界结果推广到更广泛的一类模型。然后，我们提出了一个因果效应的广义优化估计，通过求解一组扩展的方程组估计权重。在充分条件下，我们建立了因果效应估计的相合性和渐近正态性，并证明了该估计达到半参数有效界，从而将已有的关于因果效应有效估计的文献推广到更广泛的应用领域。最后，我们讨论了一些因果效应函数，如治疗效果曲线和平均结果。为了评估该方法的有限样本性能，我们进行了小规模的仿真研究，发现该估计具有实用价值。为了说明该程序的适用性，我们回顾了关于竞选广告和竞选捐款的文献。不同于现有的程序产生混合的结果，我们没有发现关于竞选捐款的竞选广告的证据。
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英文标题：
《A Unified Framework for Efficient Estimation of General Treatment Models》
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作者：
Chunrong Ai, Oliver Linton, Kaiji Motegi, 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：
  This paper presents a weighted optimization framework that unifies the binary,multi-valued, continuous, as well as mixture of discrete and continuous treatment, under the unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile and asymmetric least squares causal effect of treatment as special cases. For this general framework, we first derive the semiparametric efficiency bound for the causal effect of treatment, extending the existing bound results to a wider class of models. We then propose a generalized optimization estimation for the causal effect with weights estimated by solving an expanding set of equations. Under some sufficient conditions, we establish consistency and asymptotic normality of the proposed estimator of the causal effect and show that the estimator attains our semiparametric efficiency bound, thereby extending the existing literature on efficient estimation of causal effect to a wider class of applications. Finally, we discuss etimation of some causal effect functionals such as the treatment effect curve and the average outcome. To evaluate the finite sample performance of the proposed procedure, we conduct a small scale simulation study and find that the proposed estimation has practical value. To illustrate the applicability of the procedure, we revisit the literature on campaign advertise and campaign contributions. Unlike the existing procedures which produce mixed results, we find no evidence of campaign advertise on campaign contribution.
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PDF链接：
https://arxiv.org/pdf/1808.04936