Revisiting the Effects of Maternal Education on Adolescents' Academic Performance: Doubly Robust Estimation in a Network-Based Observational Study
重新审视母亲教育对青少年学业的影响：网络观察研究中的双重稳健估计AI 解读 ↓
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重新审视母亲教育对青少年学业的影响：网络观察研究中的双重稳健估计
- 研究问题
- 当学生成绩同时可能受到自身母亲和同伴母亲教育水平影响时，如何在社会网络干扰下估计母亲大学教育的直接与间接效应？
- 方法与思路
- 作者结合处理模型与结局模型提出双重稳健估计量，称任一模型正确时可获得一致性与渐近正态性，并使用 Add Health 网络数据分析。
- 材料陈述的贡献
- 摘要称，经验结果展示双重稳健性质及相对 IPW 的效率收益；在 Add Health 中，分析未发现母亲教育通过同伴圈影响成绩的间接效应证据。
- 阅读关注点
- 应核对网络暴露和直接、间接效应定义、干扰范围、处理联合分布及识别假设，并审查双重稳健与渐近正态结果的其他必要条件。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明网络依赖及正值性条件，未发现间接效应证据也不等于证明效应为零。
原文摘要
In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a student's academic performance is influenced both by their own mother's educational level as well as that of their peers. Since the underlying social network is measured, the Add Health study provides a unique opportunity to examine the impact of maternal college education on adolescent school performance, both direct and indirect. However, causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption no longer holds. While inverse probability-of-treatment weighted (IPW) estimators have been developed for this setting, they are often highly unstable. Motivated by the question of maternal education, we propose doubly robust (DR) estimators combining models for treatment and outcome that are consistent and asymptotically normal if either model is correctly specified. We present empirical results that illustrate the DR property and the efficiency gain of DR over IPW estimators even when the treatment model is misspecified. Contrary to previous studies, our robust analysis does not provide evidence of an indirect effect of maternal education on academic performance within adolescents' social circles in Add Health.