A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso
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结合结局自适应 Lasso 的增广逆概率加权估计量比较
- 研究问题
- 高维协变量下，倾向得分使用结局自适应 Lasso 时，结局模型应搭配何种惩罚选择方法，才能改善平均处理效应估计？
- 方法与思路
- 作者固定倾向得分模型的结局自适应 Lasso，比较结局模型的多种面向结局的惩罚选择方法，开展数值实验和临床数据分析。
- 材料陈述的贡献
- 摘要称两类模型均采用具备 oracle 性质的选择方法时模拟表现较好；临床数据中有无该性质的方法给出相近估计及标准误。
- 阅读关注点
- 应核对 oracle 性质所需条件、惩罚参数选择、模型错设和协变量结构，并检查变量选择后标准误与区间推断如何处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未展开模拟机制与识别条件，双重稳健性不能替代对选择程序的验证。
原文摘要
When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso (OAL) can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using the OAL was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the OAL to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the OAL for PS models and various outcome-oriented covariate selection via penalization for outcome models. We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. The performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was high. In a clinical trial dataset analysis, the AIPW estimators using outcome-oriented covariate selection via penalization with and without the oracle property showed similar estimates and standard errors.