Identifying causal effects in experiments with spillovers and non-compliance
存在溢出与不依从的实验中如何识别因果效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究随机实验中的直接与间接因果效应，同时允许同组他人处理产生溢出，直接支持因果推断与干扰方向。
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存在溢出与不依从的实验中如何识别因果效应
- 研究问题
- 当处理可以影响同组他人且受试者只能被提供处理机会时，如何分别识别实际参与者自身接受处理和同伴接受处理的因果效应？
- 方法与思路
- 作者使用随机饱和度实验设计，在已知组内发生溢出、参与决策不随同伴实际获邀情况变化的设定下，采用随机系数模型。
- 材料陈述的贡献
- 摘要称，可点识别参与者的直接和间接处理效应，并提出一致且渐近正态的估计量；就业服务应用发现负间接效应与正直接效应。
- 阅读关注点
- 应核对饱和度随机化机制、单侧不依从、参与决策不变性及效应对象，并检查组数与组规模共同增长的推断条件。
- 阅读边界
- 仅依据所给OpenAlex版本摘要；完整识别条件与效应数值未说明，结论依赖已知组内溢出及参与决策设定，不能直接外推到跨组干扰。
原文摘要
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 within known groups, and take-up decisions are invariant to peers’ realized offers. In this setting we point identify the effects of treatment-on-the-treated, both direct and indirect, in a flexible random coefficients model that allows for heterogeneous treatment effects and endogenous 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.