Quantile regression outcome-adaptive lasso: variable selection for causal quantile treatment effect estimation
分位数回归结局自适应 lasso：因果分位数处理效应的变量选择AI 解读 ↓
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分位数回归结局自适应 lasso：因果分位数处理效应的变量选择
- 研究问题
- 非随机研究中，不同结局分位数可能需要不同的倾向得分协变量；如何选择变量以平衡分位数处理效应估计的偏差与效率？
- 方法与思路
- 作者用线性分位数回归构造 lasso 惩罚权重，对不同分位数分别选择倾向得分模型协变量，并开展模拟与 CHARLS 应用。
- 材料陈述的贡献
- 摘要称 QROAL 在变量选择和若干同质、异质设计的均方根误差上优于 OAL，并用于分析吸烟与抑郁症状严重程度。
- 阅读关注点
- 应核对不同分位数下的调整集与识别条件、惩罚权重和调参步骤，以及变量选择后的效应估计与不确定性处理。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出无偏高效所需条件，模拟优势也未提供具体数值。
原文摘要
Quantile treatment effects (QTEs) can characterize the potentially heterogeneous causal effect of a treatment on different points of the entire outcome distribution. Propensity score (PS) methods are commonly employed for estimating QTEs in non-randomized studies. Empirical and theoretical studies have shown that insufficient and unnecessary adjustment for covariates in PS models can lead to bias and efficiency loss in estimating treatment effects. Striking a balance between bias and efficiency through variable selection is a crucial concern in casual inference. It is essential to acknowledge that the covariates related treatment and outcome may vary across different quantiles of the outcome distribution. However, previous studies have overlooked to adjust for different covariates separately in the PS models when estimating different QTEs. In this article, we proposed the quantile regression outcome-adaptive lasso (QROAL) method to select covariates that can provide unbiased and efficient estimates of QTEs. A distinctive feature of our proposed method is the utilization of linear quantile regression models for constructing penalty weights, enabling covariate selection in PS models separately when estimating different QTEs. We conducted simulation studies to show the superiority of our proposed method over the outcome-adaptive lasso (OAL) method in variable selection. Moreover, the proposed method exhibited favorable performance compared to the OAL method in terms of root mean square error in a range of settings, including both homogeneous and heterogeneous scenarios. Additionally, we applied the QROAL method to datasets from the China Health and Retirement Longitudinal Study (CHARLS) to explore the impact of smoking status on the severity of depression symptoms.