Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure
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网络结构未知时随机实验的总体处理效应估计
- 研究问题
- 当个体处理会影响邻居结果且干扰网络未知时，随机实验如何估计目标总体的处理效应，并避免传统方法的偏误？
- 方法与思路
- 作者在异质性加性网络效应的潜在结果模型中，利用实验前历史基线均值，构造无需获知网络的估计量与随机设计。
- 材料陈述的贡献
- 摘要称论文刻画未知网络下的估计限制，并在所设模型及历史基线信息下提供无偏、低方差估计和统计保证。
- 阅读关注点
- 应核对总体处理效应的定义、加性网络模型的限制、历史基线的可用性要求，以及无偏性和方差保证依赖的随机设计。
- 阅读边界
- 这是arXiv预印本，同行评审与期刊接收未核实；摘要未说明基线误差及网络限制，不能外推至任意干扰机制。
原文摘要
Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological sciences, from the social sciences to engineering, to public policy and to the technology industry at large. Here, we consider situations where classical methods for estimating the total treatment effect on a target population are considerably biased due to confounding network effects, i.e., the fact that the treatment of an individual may impact their neighbors' outcomes, an issue referred to as network interference or as non-individualized treatment response. A key challenge in these situations, is that the network is often unknown, and difficult, or costly, to measure. In this paper, we characterize the limitations in estimating the total treatment effect without knowledge of the network that drives interference, assuming a potential outcomes model with heterogeneous additive network effects. This model encompasses a broad class of network interference sources, including spillover, peer effects, and contagion. Within this framework, we show that, surprisingly, given access to average historical baseline measurements prior to the experiment, we can develop a simple estimator and efficient randomized design that outputs an unbiased estimate with low variance. Our solution does not require knowledge of the underlying network structure, and it comes with statistical guarantees for a broad class of models. We believe our results are poised to impact current randomized experimentation strategies due to its ease of interpretation and implementation, alongside its provable theoretical insights under heterogeneous network effects.