部分干扰下倾向得分估计的混合效应结果自适应Lasso方法
Mixed-effects Outcome-Adaptive Lasso for Propensity Score Estimation under Partial Interference
AI总结：
本研究针对部分干扰下倾向得分估计的问题，提出混合效应结果自适应Lasso方法，可稳定估计因果效应，提升IPW估计量效率，经模拟与DHS疟疾数据验证有效。
AI中文摘要：
当一个个体的处理或暴露影响另一个个体的结局时，就会发生干扰。特别地，本文假设存在部分干扰，即个体被划分为不同组，不同组的个体之间不存在干扰。在观察性研究中，基于倾向得分的逆概率加权（IPW）常被用于因果效应估计。但在部分干扰下，必须估计组水平的倾向得分，且其比常规个体水平倾向得分更易取极端值，导致IPW估计量方差较大；当存在大量协变量时，该问题会更严重。本研究提出一种基于混合效应logistic回归模型的结果自适应Lasso方法，用于在部分干扰下稳定估计因果效应。该方法在考虑处理分配中未观测到的组水平异质性的同时，对倾向得分模型进行协变量选择与估计。在正则条件下，证明该方法具有oracle性质，且基于该方法的IPW估计量是一致且渐近正态的。通过蒙特卡洛模拟，验证该方法能以高频率选择混杂因子和预后因子，同时排除工具变量和虚假变量；结果进一步表明该方法可提升IPW估计量的有限样本效率。此外，采用刚果民主共和国人口与健康调查（DHS）的疟疾数据评估了所提方法的性能。
英文摘要：
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).
