G-computation for causal effect estimation from observational hierarchical data with unmeasured cluster context
未观测群组情境下层级观测数据的因果效应 g-computationAI 解读 ↓
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未观测群组情境下层级观测数据的因果效应 g-computation
- 研究问题
- 当个体嵌套于群组，未观测群组因素既造成混杂又可能引起处理效应异质性时，如何降低观测数据因果效应估计的偏误？
- 方法与思路
- 作者以随机效应模型拟合结果，并按群组的观测处理比例将群组分层，在各层内进行 g-computation 后聚合效应估计。
- 材料陈述的贡献
- 摘要称模拟中随机效应方法降低部分情境的偏误，分层版本在混杂兼效应异质性情境下均方根误差最低，并展示孟加拉国应用。
- 阅读关注点
- 应核对处理比例分层的依据、随机效应与混杂变量的关系、模拟设定，以及应用中的识别假设和 bootstrap 区间构造。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；模拟中的偏误改善不能保证消除任意未观测混杂，摘要未详述识别条件。
原文摘要
Observational studies frequently involve hierarchical data structures in which individuals are nested within higher-level units. In such settings, unmeasured cluster-level factors may confound the treatment-outcome relationship and may additionally induce treatment effect heterogeneity across clusters, complicating causal effect estimation. We formalize the use of g-computation for hierarchical observational data by incorporating random-effects models (REM) as outcome models and propose a within-group g-computation strategy designed to reduce bias arising from unmeasured cluster context. The approach groups clusters according to their observed treatment prevalence and performs g-computation within groups before aggregating group-specific estimates. Through extensive Monte Carlo simulations, we compare the standard and within-group implementations of g-computation using both linear models and REM. Results show that both standard and within-group REM-based implementations substantially reduce bias when the unmeasured cluster-level variable acts solely as a confounder, whereas the proposed within-group REM estimator achieves the lowest RMSE when the unmeasured cluster-level factor acts as both a confounder and a source of treatment effect heterogeneity. We apply the proposed within-group REM estimator to estimate the causal effect of adolescent pregnancy on the child height-for-age Z-score using 2019 Bangladesh MICS data, obtaining an estimated effect of -0.12 (95% bootstrap CI: [-0.18, -0.06]). The proposed within-group g-computation framework offers a strategy for reducing bias from unmeasured cluster-level confounding and treatment effect heterogeneity in hierarchical observational studies.