摘要翻译：
在一个统一的框架中，当感兴趣的结果，通常是一个持续时间，受到右审查时，我们为各种治疗效果提供估计量和置信度带。我们的方法在不同的识别假设下，包括不混杂性、局部处理效应和非线性差异中的差异，适应平均、分布和分位数处理效应。所提出的估计量易于实现，具有接近形式的表示，完全基于对干扰参数估计的数据驱动，并且不依赖于参数分布假设、形状限制或对不同子群体潜在治疗效果异质性的限制。这些处理效应结果是关于两步Kaplan-Meier估计的更一般结果的结果，这些结果是独立感兴趣的：我们提供了应用(i)统一大数定律，(ii)泛函中心极限定理，(iii)在感兴趣的结果可以随机删失的两步估计过程中普通非参数bootstrap的有效性的条件。
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英文标题：
《Program Evaluation with Right-Censored Data》
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作者：
Pedro H. C. Sant'Anna
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最新提交年份：
2016
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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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：
  In a unified framework, we provide estimators and confidence bands for a variety of treatment effects when the outcome of interest, typically a duration, is subjected to right censoring. Our methodology accommodates average, distributional, and quantile treatment effects under different identifying assumptions including unconfoundedness, local treatment effects, and nonlinear differences-in-differences. The proposed estimators are easy to implement, have close-form representation, are fully data-driven upon estimation of nuisance parameters, and do not rely on parametric distributional assumptions, shape restrictions, or on restricting the potential treatment effect heterogeneity across different subpopulations. These treatment effects results are obtained as a consequence of more general results on two-step Kaplan-Meier estimators that are of independent interest: we provide conditions for applying (i) uniform law of large numbers, (ii) functional central limit theorems, and (iii) we prove the validity of the ordinary nonparametric bootstrap in a two-step estimation procedure where the outcome of interest may be randomly censored.
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PDF链接：
https://arxiv.org/pdf/1604.02642