摘要翻译：
本文展示了如何使用随机饱和实验设计来识别和估计存在溢出效应时的因果效应--一个人的治疗可能影响另一个人的结果--以及片面的不依从性--受试者只能接受治疗，而不是被迫接受治疗。在这种情况下，两种不同的因果效应引起了人们的兴趣：直接效应量化了一个人自己的治疗如何改变她的结果，而间接效应量化了她同伴的治疗如何改变她的结果。我们考虑了这样一种情况，即溢出仅发生在已知的群体中，并且吸收决策不依赖于同行的提议。在这种情况下，我们指出，在一个灵活的随机系数模型中，可以识别直接和间接的局部平均处理效应，该模型允许异源处理效应和内源选择进入处理。我们进一步提出了一个可行的估计量，它是一致的和渐近正态的，随着群的数目和规模的增加。我们将我们的估计量应用于一项大规模就业安置服务实验的数据，并发现对那些愿意接受该项目的人来说，负面的间接待遇影响了就业的可能性。这些负面的溢出效应被自身吸收的正面直接处理效应所抵消。
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英文标题：
《Identifying Causal Effects in Experiments with Spillovers and
Non-compliance》
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作者：
Francis J. DiTraglia (1), Camilo Garcia-Jimeno (2), Rossa
O\'Keeffe-O\'Donovan (1), and Alejandro Sanchez-Becerra (3) ((1) Department of
Economics University of Oxford, (2) Federal Reserve Bank of Chicago, (3)
University of Pennsylvania)
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最新提交年份：
2021
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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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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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英文摘要：
This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of spillovers--one person\'s treatment may affect another\'s outcome--and one-sided non-compliance--subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person\'s own treatment changes her outcome, while indirect effects quantify how her peers\' treatments change her outcome. We consider the case in which spillovers occur only within known groups, and take-up decisions do not depend on peers\' offers. In this setting we point identify local average treatment effects, both direct and indirect, in a flexible random coefficients model that allows for both heterogenous treatment effects and endogeneous selection into treatment. We go on to propose a feasible estimator that is consistent and asymptotically normal as the number and size of groups increases. We apply our estimator to data from a large-scale job placement services experiment, and find negative indirect treatment effects on the likelihood of employment for those willing to take up the program. These negative spillovers are offset by positive direct treatment effects from own take-up.
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