Mixed-effects Outcome-Adaptive Lasso for Propensity Score Estimation under Partial Interference
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部分干扰下用于倾向得分估计的混合效应结果自适应 Lasso
- 研究问题
- 在组内存在干扰、组间无干扰且协变量较多时，如何避免组层面倾向得分极端值造成的高方差，稳定估计因果效应？
- 方法与思路
- 作者以混合效应逻辑回归构造结果自适应 Lasso，同步进行倾向得分变量选择与估计，纳入处理分配中的未观测组层面异质性。
- 材料陈述的贡献
- 摘要称，正则条件下方法具有 oracle 性质，所得 IPW 估计一致且渐近正态；模拟显示变量选择和有限样本效率改善，并应用于疟疾数据。
- 阅读关注点
- 应核对部分干扰下的效应定义、组倾向得分与随机效应假设，以及结果自适应权重、重叠条件和变量选择后的推断。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列出识别及正则条件，纳入组随机效应不自动消除所有未观测混杂。
原文摘要
Interference occurs when one individual's treatment or exposure affects another individual's outcome. In particular, we assume partial interference, where individuals are divided into groups such that there is no interference between individuals in different groups. In observational studies, inverse probability weighting (IPW) based on propensity scores is often used for causal effect estimation. However, under partial interference, the group-level propensity score must be estimated, and it is more likely to take extreme values than the usual individual-level propensity score. As a result, IPW estimators may have large variances. This problem can become more serious when many covariates are available. In this study, we propose an Outcome-Adaptive Lasso based on a mixed-effects logistic regression model to stably estimate causal effects under partial interference. The proposed method performs covariate selection and estimation in the propensity score model simultaneously while accounting for unobserved group-level heterogeneity in treatment assignment. Under regularity conditions, we show that the proposed method has the oracle property and that the IPW estimators based on the proposed method are consistent and asymptotically normal. Through Monte Carlo simulations, we demonstrate that the proposed method tends to select confounders and prognostic factors at high frequencies, while excluding instrumental variables and spurious variables. The results further suggest that the proposed method improves the finite-sample efficiency of IPW estimators. We evaluate the performance of the proposed method using malaria data from the Democratic Republic of the Congo Demographic and Health Survey (DHS).