Causal Effects with Hidden Treatment Diffusion on Observed or Partially Observed Networks
观测或部分观测网络中隐藏处理扩散的因果效应AI 解读 ↓
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观测或部分观测网络中隐藏处理扩散的因果效应
- 研究问题
- 随机实验中处理物品可能沿同伴网络传播，使原对照个体实际接受处理时，如何评估这种隐藏扩散对因果效应估计的影响？
- 方法与思路
- 作者针对观测或部分观测网络提出模拟型敏感性分析，在合理的敏感参数范围内生成扩散情景，比较各情景与忽略扩散时的效应估计。
- 材料陈述的贡献
- 摘要称，该框架可检验效应估计对隐藏扩散的稳健性；受学校激励实地实验启发的结果提示，即使较小扩散参数也可能造成明显偏差。
- 阅读关注点
- 应核对扩散机制、敏感参数范围的依据、部分网络观测的处理，以及模拟中实际接受处理与原始分配如何决定效应估计。
- 阅读边界
- 所给版本为 arXiv 预印本，同行评审与期刊接收未核实；摘要未说明扩散参数可否识别，情景敏感性分析不能直接确认真实扩散程度。
原文摘要
In randomized experiments, interactions between units might generate a treatment diffusion process. This is common when the treatment of interest is an actual object or product that can be shared among peers (e.g., flyers, booklets, videos). For instance, if the intervention of interest is an information campaign realized through the distribution of a video to targeted individuals, some of these treated individuals might share the video they received with their friends. Such a phenomenon is usually unobserved, causing a misallocation of individuals in the two treatment arms: some of the initially untreated units might have actually received the treatment by diffusion. Treatment misclassification can, in turn, introduce a bias in the estimation of the causal effect. Inspired by a recent field experiment on the effect of different types of school incentives aimed at encouraging students to attend cultural events, we present a novel approach to deal with a hidden diffusion process on observed or partially observed networks.Specifically, we develop a simulation-based sensitivity analysis that assesses the robustness of the estimates against the possible presence of a treatment diffusion. We simulate several diffusion scenarios within a plausible range of sensitivity parameters and we compare the treatment effect which is estimated in each scenario with the one that is obtained while ignoring the diffusion process. Results suggest that even a treatment diffusion parameter of small size may lead to a significant bias in the estimation of the treatment effect.