Identifying average causal effect in regression discontinuity design with auxiliary data
借助辅助数据识别断点回归设计中的平均因果效应AI 解读 ↓
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借助辅助数据识别断点回归设计中的平均因果效应
- 研究问题
- 断点回归中，处理由运行变量确定而缺乏通常的正值性；能否借助辅助变量和额外数据识别超出阈值局部范围的平均处理效应？
- 方法与思路
- 作者引入运行变量可作为其代理的辅助变量及包含两者的额外数据，并提出类似结果回归、逆概率加权和双重稳健的三类估计方法。
- 材料陈述的贡献
- 摘要称，该框架支持平均处理效应识别及渐近有效推断；模拟展示方法表现，维生素 A 应用报告正向但统计不显著的效应。
- 阅读关注点
- 应核对辅助变量与运行变量之间的代理关系、两份数据的衔接条件，以及从阈值局部效应扩展到平均效应所需的识别限制。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明完整识别假设，应用结果也不能据此确立显著疗效。
原文摘要
Regression discontinuity designs are widely used when treatment assignment is determined by whether a running variable exceeds a predefined threshold. However, most research focuses on estimating local causal effects at the threshold, leaving the challenge of identifying treatment effects away from the cutoff largely unaddressed. The primary difficulty in this context is that the treatment assignment is deterministically defined by the running variable, violating the commonly assumed positivity assumption. In this paper, we introduce a novel framework for identifying the average causal effect in regression discontinuity designs. Our approach assumes the existence of an auxiliary variable for which the running variable can be seen as a surrogate, and an additional dataset that consists of the running variable and the auxiliary variable alongside the traditional regression discontinuity design setup. Under this framework, we propose three estimation methods for the ATE, which resembles the outcome regression, inverse propensity weighted and doubly robust estimators in classical causal inference literature. Asymptotically valid inference procedures are also provided. To demonstrate the practical application of our method, simulations are conducted to show the good performance of our methods; besides, we use the proposed methods to assess the causal effects of vitamin A supplementation on the severity of autism spectrum disorders in children, where a positive effect is found but with no statistical significance.